{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 特别注意事项\n",
    "## scipy的kurtosis包计算的是excess kurtosis。也就是说：如果数据满足正态分布，其值应该是零，而不是3.\n",
    "## 收益率一定要乘100，否则数据太小做估计的时候，效果会很差，迭代甚至有不运行的情况。而且liki应该是正值，不应该是负值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 4091 entries, 2000-01-05 00:00:00 to 2017-08-18 00:00:00\n",
      "Data columns (total 6 columns):\n",
      "SP500       4091 non-null float64\n",
      "USDRMB      4091 non-null float64\n",
      "SHINDEX     4091 non-null float64\n",
      "SZINDEX     4091 non-null float64\n",
      "USDINDEX    4091 non-null float64\n",
      "EURUSD      4091 non-null float64\n",
      "dtypes: float64(6)\n",
      "memory usage: 223.7+ KB\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 4091 entries, 2000-01-05 00:00:00 to 2017-08-18 00:00:00\n",
      "Data columns (total 6 columns):\n",
      "SP500       4091 non-null float64\n",
      "USDRMB      4091 non-null float64\n",
      "SHINDEX     4091 non-null float64\n",
      "SZINDEX     4091 non-null float64\n",
      "USDINDEX    4091 non-null float64\n",
      "EURUSD      4091 non-null float64\n",
      "dtypes: float64(6)\n",
      "memory usage: 223.7+ KB\n"
     ]
    },
    {
     "data": {
      "image/png": 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Tk5yS5L4kO5LcleSqJM9ollmdpJp5M6/Xda0zSd6Y5HvN641J0jV/dZLPJfmn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\nUouYnEmSJLXI/wf1s+vcwLWpYwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c227d8f630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#-*- coding:utf-8 -*-\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy.stats as ss\n",
    "import statsmodels.api as sm\n",
    "import scipy as sp\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.optimize import fmin_slsqp\n",
    "%matplotlib inline\n",
    "df = pd.read_excel(\"yyf_prices.xls\",parse_dates=[0])\n",
    "df.index=df.pop('Date')\n",
    "df.hist(figsize=[10,10])\n",
    "\n",
    "df_pct_rets = 100* df.pct_change().dropna()\n",
    "df_log_rets = 100*np.log(df.dropna()/df.dropna().shift(1)).dropna()\n",
    "\n",
    "df_pct_rets.info()\n",
    "df_log_rets.info()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000001C037DF96D8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001C03B1D78D0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000001C039B39A90>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001C03B2CF240>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000001C03B1F9A58>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001C03B70D668>]], dtype=object)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3vZU91uZnlj9BkguACwAmJiaYmppavqhHzK5du1ZN/TaesHtJ20/sv/R9LMQwf66r6bwu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\ndcwETJIkqWP/Dzot6vGRajXsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c037c292e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_pct_rets.hist(figsize=[10,10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000001C039E52F60>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001C039DBDE48>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000001C039F78978>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001C03918D588>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000001C03940C588>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001C039691AC8>]], dtype=object)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wN8ATgLcBf5LkgGFvn6Rdx1z9WVU9ta+v2gv4SeCbwNmzLhAeBH57jvkAL66q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\nSeoIEzNJkqSO+H8DXYnD207CFgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c03b545358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_log_rets.hist(figsize=[10,10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SP500</th>\n",
       "      <th>USDRMB</th>\n",
       "      <th>SHINDEX</th>\n",
       "      <th>SZINDEX</th>\n",
       "      <th>USDINDEX</th>\n",
       "      <th>EURUSD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4091.000000</td>\n",
       "      <td>4091.000000</td>\n",
       "      <td>4091.000000</td>\n",
       "      <td>4091.000000</td>\n",
       "      <td>4091.000000</td>\n",
       "      <td>4091.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.013444</td>\n",
       "      <td>-0.005269</td>\n",
       "      <td>0.020616</td>\n",
       "      <td>0.027139</td>\n",
       "      <td>0.001059</td>\n",
       "      <td>0.003254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.265054</td>\n",
       "      <td>0.108506</td>\n",
       "      <td>1.650006</td>\n",
       "      <td>1.856530</td>\n",
       "      <td>0.327590</td>\n",
       "      <td>0.669761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-13.777424</td>\n",
       "      <td>-2.032231</td>\n",
       "      <td>-12.763626</td>\n",
       "      <td>-12.531498</td>\n",
       "      <td>-2.301435</td>\n",
       "      <td>-8.027087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.513461</td>\n",
       "      <td>-0.024494</td>\n",
       "      <td>-0.701659</td>\n",
       "      <td>-0.835951</td>\n",
       "      <td>-0.168847</td>\n",
       "      <td>-0.370071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.053344</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.065536</td>\n",
       "      <td>0.053346</td>\n",
       "      <td>-0.000334</td>\n",
       "      <td>0.011009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.579883</td>\n",
       "      <td>0.016210</td>\n",
       "      <td>0.803561</td>\n",
       "      <td>0.932888</td>\n",
       "      <td>0.164335</td>\n",
       "      <td>0.367260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10.423558</td>\n",
       "      <td>1.840324</td>\n",
       "      <td>9.400973</td>\n",
       "      <td>10.752491</td>\n",
       "      <td>4.332160</td>\n",
       "      <td>3.748129</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             SP500       USDRMB      SHINDEX      SZINDEX     USDINDEX  \\\n",
       "count  4091.000000  4091.000000  4091.000000  4091.000000  4091.000000   \n",
       "mean      0.013444    -0.005269     0.020616     0.027139     0.001059   \n",
       "std       1.265054     0.108506     1.650006     1.856530     0.327590   \n",
       "min     -13.777424    -2.032231   -12.763626   -12.531498    -2.301435   \n",
       "25%      -0.513461    -0.024494    -0.701659    -0.835951    -0.168847   \n",
       "50%       0.053344     0.000000     0.065536     0.053346    -0.000334   \n",
       "75%       0.579883     0.016210     0.803561     0.932888     0.164335   \n",
       "max      10.423558     1.840324     9.400973    10.752491     4.332160   \n",
       "\n",
       "            EURUSD  \n",
       "count  4091.000000  \n",
       "mean      0.003254  \n",
       "std       0.669761  \n",
       "min      -8.027087  \n",
       "25%      -0.370071  \n",
       "50%       0.011009  \n",
       "75%       0.367260  \n",
       "max       3.748129  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_log_rets.describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SP500      -0.425268\n",
      "USDRMB      0.669598\n",
      "SHINDEX    -0.380678\n",
      "SZINDEX    -0.348180\n",
      "USDINDEX    0.441793\n",
      "EURUSD     -0.389239\n",
      "dtype: float64\n",
      "SP500       10.727590\n",
      "USDRMB      71.715019\n",
      "SHINDEX      5.176679\n",
      "SZINDEX      4.006025\n",
      "USDINDEX    11.262928\n",
      "EURUSD       6.577719\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(df_log_rets.skew())\n",
    "print(df_log_rets.kurt())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SP500</th>\n",
       "      <th>USDRMB</th>\n",
       "      <th>SHINDEX</th>\n",
       "      <th>SZINDEX</th>\n",
       "      <th>USDINDEX</th>\n",
       "      <th>EURUSD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4091.000000</td>\n",
       "      <td>4091.000000</td>\n",
       "      <td>4091.000000</td>\n",
       "      <td>4091.000000</td>\n",
       "      <td>4091.000000</td>\n",
       "      <td>4091.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.015030</td>\n",
       "      <td>-0.005235</td>\n",
       "      <td>0.034246</td>\n",
       "      <td>0.045117</td>\n",
       "      <td>0.003125</td>\n",
       "      <td>0.005756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.210493</td>\n",
       "      <td>0.106656</td>\n",
       "      <td>1.604210</td>\n",
       "      <td>1.799297</td>\n",
       "      <td>0.310703</td>\n",
       "      <td>0.636947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-9.034980</td>\n",
       "      <td>-2.011720</td>\n",
       "      <td>-8.840626</td>\n",
       "      <td>-9.289852</td>\n",
       "      <td>-2.275154</td>\n",
       "      <td>-2.728324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.508803</td>\n",
       "      <td>-0.023634</td>\n",
       "      <td>-0.693534</td>\n",
       "      <td>-0.821900</td>\n",
       "      <td>-0.164869</td>\n",
       "      <td>-0.370007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.048817</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.066510</td>\n",
       "      <td>0.048859</td>\n",
       "      <td>0.001699</td>\n",
       "      <td>0.008295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.567063</td>\n",
       "      <td>0.016144</td>\n",
       "      <td>0.792535</td>\n",
       "      <td>0.921329</td>\n",
       "      <td>0.162739</td>\n",
       "      <td>0.360728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10.789002</td>\n",
       "      <td>1.857362</td>\n",
       "      <td>9.857043</td>\n",
       "      <td>9.998804</td>\n",
       "      <td>1.751054</td>\n",
       "      <td>3.819258</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             SP500       USDRMB      SHINDEX      SZINDEX     USDINDEX  \\\n",
       "count  4091.000000  4091.000000  4091.000000  4091.000000  4091.000000   \n",
       "mean      0.015030    -0.005235     0.034246     0.045117     0.003125   \n",
       "std       1.210493     0.106656     1.604210     1.799297     0.310703   \n",
       "min      -9.034980    -2.011720    -8.840626    -9.289852    -2.275154   \n",
       "25%      -0.508803    -0.023634    -0.693534    -0.821900    -0.164869   \n",
       "50%       0.048817     0.000000     0.066510     0.048859     0.001699   \n",
       "75%       0.567063     0.016144     0.792535     0.921329     0.162739   \n",
       "max      10.789002     1.857362     9.857043     9.998804     1.751054   \n",
       "\n",
       "            EURUSD  \n",
       "count  4091.000000  \n",
       "mean      0.005756  \n",
       "std       0.636947  \n",
       "min      -2.728324  \n",
       "25%      -0.370007  \n",
       "50%       0.008295  \n",
       "75%       0.360728  \n",
       "max       3.819258  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pct_rets.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SP500      -0.120503\n",
      "USDRMB      0.730589\n",
      "SHINDEX    -0.154222\n",
      "SZINDEX    -0.166664\n",
      "USDINDEX    0.017948\n",
      "EURUSD      0.063156\n",
      "dtype: float64\n",
      "SP500        7.113175\n",
      "USDRMB      76.432671\n",
      "SHINDEX      4.752923\n",
      "SZINDEX      3.489555\n",
      "USDINDEX     4.345866\n",
      "EURUSD       1.661060\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(df_pct_rets.skew())\n",
    "print(df_pct_rets.kurt())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1c03b856b70>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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H1vgR11uKzxkm0rqJ0SqDmNOw+5Dir2Iee9Hkq77YCg1MqaK5qPoF1UCMPVU0Kz6LVB3f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\nUCgUFxD/H6PZlBEslKYdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c03b68f128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statsmodels.tsa.stattools import acf\n",
    "SP500_acf=acf(df_log_rets.SP500, nlags=200)\n",
    "tmp=(df_log_rets.SP500)**2\n",
    "SP500_acf2=acf(tmp, nlags=200)\n",
    "\n",
    "plt.figure(figsize=(8,7),dpi=980)\n",
    "\n",
    "p1 = plt.subplot(2,1,1)\n",
    "p1.grid(True)\n",
    "p1.plot(SP500_acf[1:],color='#009CD1')\n",
    "p1.set_title('SP500 ACF of Returns',fontsize=10)\n",
    "\n",
    "p2 = plt.subplot(2,1,2)\n",
    "p2.grid(True)\n",
    "p2.plot(SP500_acf2[1:],color='#8E008D')\n",
    "p2.set_title('SP500 ACF of Returns$^{2}$',fontsize=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c03ba94ac8>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VtJ85rWhcAj+az5xh78BBnPnmG/K++kpFQKgEZpuiCdcrGpcVjYcQR/mbN3P8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\nKBQKRb2iFI1CoVAo6pX/D+vGtCt7/GOxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c03ba944a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1c03b678748>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dNBgY2Lfsb8aH5mDsPABbb7yVgZv6GjpW4hsnuKYjwcDA3YD3XLb87RHs/m2U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\nzrkS+QcN9bSvXPC9BAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c037d41898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "SZ_acf=acf(df_log_rets.SZINDEX, nlags=200)\n",
    "tmp=(df_log_rets.SZINDEX)**2\n",
    "SZ_acf2=acf(tmp, nlags=200)\n",
    "\n",
    "plt.figure(figsize=(8,7),dpi=980)\n",
    "\n",
    "p1 = plt.subplot(2,1,1)\n",
    "p1.grid(True)\n",
    "p1.plot(SZ_acf[1:],color='#009CD1')\n",
    "p1.set_title('SHENZHEN ACF of Returns',fontsize=10)\n",
    "\n",
    "p2 = plt.subplot(2,1,2)\n",
    "p2.grid(True)\n",
    "p2.plot(SZ_acf2[1:],color='#8E008D')\n",
    "p2.set_title('SHENZHEN ACF of Returns$^{2}$',fontsize=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1c03b8d1940>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fjuB4DZlZxkLFwr+dmsOPRrIAnFz0nbYx95Pjp4qGmIe4QtcV0VZUAeH+9a3t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\nG7skSQX5Btv09l1UCmS5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c0486f8b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "EURUSD_acf=acf(df_log_rets.EURUSD, nlags=200)\n",
    "tmp=(df_log_rets.EURUSD)**2\n",
    "EURUSD_acf2=acf(tmp, nlags=200)\n",
    "\n",
    "plt.figure(figsize=(8,7),dpi=980)\n",
    "\n",
    "p1 = plt.subplot(2,1,1)\n",
    "p1.grid(True)\n",
    "p1.plot(EURUSD_acf[1:],color='#009CD1')\n",
    "p1.set_title('EURUSD ACF of Returns',fontsize=10)\n",
    "\n",
    "p2 = plt.subplot(2,1,2)\n",
    "p2.grid(True)\n",
    "p2.plot(EURUSD_acf2[1:],color='#8E008D')\n",
    "p2.set_title('EURUSD ACF of Returns$^{2}$',fontsize=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 下面演示随机生成一个正态分布的列向量，然后绘制它的直方图\n",
    "## 特别需要注意，numpy坑爹的sort命令，sort默认只做行向量的sort，列的sort它不做，也就是说如果每行只有一个元素，则调用sort命令等于是不sort任何数据，等于是不工作\n",
    "## 解决办法是把sort命令里面的属性axis设置为0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\statsmodels\\compat\\pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
      "  from pandas.core import datetools\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([-0.08431554])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1XNc1rV2JhY9HeKFKEfEWT66x3wt86sH5pAByhnr506cY+tMn9Fv1JQdDqnL/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\nEQkwCnYRkQCjYBcRCTD/D9498pgrewmdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c22750fc88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#def mynormqqplot(data):\n",
    "#s_rts = np.sort(df_log_rets.SP500)   \n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy.stats as ss\n",
    "import statsmodels.api as sm\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.optimize import fmin_slsqp\n",
    "%matplotlib inline\n",
    "tmpdata=np.random.randn(10000,1)\n",
    "# more about sort function, be careful the axis\n",
    "s_rts = np.sort(tmpdata,axis=0)\n",
    "idx_s_rts=np.argsort(tmpdata)  \n",
    "len_s_rts=len(s_rts)\n",
    "\n",
    "norm_quant_rts=np.zeros([len_s_rts,1])\n",
    "\n",
    "\n",
    "for i in range(0,len_s_rts):\n",
    "    norm_quant_rts[i]=ss.norm.ppf((i+1.0-0.5)/len_s_rts)\n",
    "\n",
    "plt.scatter(norm_quant_rts,s_rts)\n",
    "\n",
    "min_qt=np.min(norm_quant_rts)\n",
    "min_s=np.min(s_rts)\n",
    "min_ax =np.min([min_qt,min_s])\n",
    "\n",
    "max_qt=np.max(norm_quant_rts)\n",
    "max_s=np.max(s_rts)\n",
    "max_ax =np.max([max_qt,max_s])\n",
    "\n",
    "ax_x=np.linspace(min_ax,max_ax,len_s_rts)\n",
    "ax_y=np.linspace(min_ax,max_ax,len_s_rts)\n",
    "plt.plot(ax_x,ax_y,color='r')\n",
    "plt.grid(True)\n",
    "\n",
    "ss.kurtosis(tmpdata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.19435264])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ss.kurtosis(tmpdata)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 下面回到我们前面的讨论的例子，继续用我们的数据\n",
    "## 我们定义一个函数，专门用来绘制qq plot\n",
    "## 需要注意一下，这是我们第一次在Python里面定义一个函数\n",
    "## 然后再在后面调用它\n",
    "## 当让这个函数写的也非常的简单，功能非常不全，比如我们没有专门的功能对title这些进行标注\n",
    "## 此外在运行前请restart terminal "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1c2283d42e8>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Keaa8EonUV23dWzn79jBq3StMKCnivLeX0q5iD2Vt2jO3/wjm5B1eBv7uSwcx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\nWwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c227d2df28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c229695cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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05BM20COcz7eUxziDT+kQ2+k0Uquw1Rso7v6NXBYiIs3EunUwbRokElTNr6CtV7GGfiT5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\nKCIiEgsFioiIxOL/A5kOso4fdOFXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c2283c2080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy.stats as ss\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "def mynormqqplot(data):\n",
    "    std_data=data.std()\n",
    "    s_rts = np.sort(data,axis=0)\n",
    "    len_s_rts=len(s_rts)\n",
    "    norm_quant_rts=np.zeros([len_s_rts,1])\n",
    "    for i in range(0,len_s_rts):\n",
    "        norm_quant_rts[i]=ss.norm.ppf((i+1.0-0.5)/len_s_rts)\n",
    "    \n",
    "    plt.figure()\n",
    "    plt.scatter(norm_quant_rts,s_rts)\n",
    "    \n",
    "    min_qt=np.min(norm_quant_rts)\n",
    "    min_s=np.min(s_rts)\n",
    "    min_ax =np.min([min_qt,min_s])\n",
    "    \n",
    "    max_qt=np.max(norm_quant_rts)\n",
    "    max_s=np.max(s_rts)\n",
    "    max_ax =np.max([max_qt,max_s])\n",
    "    \n",
    "    ax_x=np.linspace(min_ax,max_ax,len_s_rts)\n",
    "    ax_y=std_data*ax_x #std_data is like a slope\n",
    "    plt.plot(ax_x,ax_y,'-',color='r')\n",
    "    plt.grid(True)\n",
    "    plt.xlabel('theoretical quantiles')\n",
    "    plt.ylabel('sample quantiles')\n",
    "# mynormqqplot function END\n",
    "\n",
    "df = pd.read_excel(\"yyf_prices.xls\",parse_dates=[0])\n",
    "df.index=df.pop('Date')\n",
    "#df.hist(figsize=[10,10])\n",
    "\n",
    "df_log_rets =  np.log(df.dropna()/df.dropna().shift(1)).dropna()\n",
    "\n",
    "std_rts=df_log_rets.SHINDEX.std()\n",
    "normalized_log_rts=np.asarray(df_log_rets.SHINDEX/std_rts)\n",
    "\n",
    "# call our function ‘mynormqqplot’\n",
    "mynormqqplot(np.asarray(df_log_rets.SHINDEX))\n",
    "plt.title('mynormqqplt result')\n",
    "\n",
    "plt.figure()\n",
    "sm.qqplot(df_log_rets.SHINDEX,line='s')\n",
    "plt.grid(True)\n",
    "plt.xlabel('theoretical quantiles')\n",
    "plt.ylabel('sample quantiles')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 从上图可以看出，对数收益率只是看起来象正态分布，但是确实不是正态分布\n",
    "## 尾部数据明显不满足正态分布"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 下面我们开始来讨论如何实现Garch11\n",
    "# 请在运行前，restart engine!!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type     || mean|| std|| skew|| kurt||\n",
      "Original data:   0.014618164125 0.999877773024 -0.3480523513260165 3.9996646163169807\n",
      "After Garch:   0.0157070647517 1.0 -0.27938920896902253 3.4287101661194397\n"
     ]
    },
    {
     "data": {
      "image/png": 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g7Px/twD4VbUKRURERFTPOOYgniIdHAghxgG4FsDDLpvcAOAxmTMfwLFCiJOrVkAiIiKi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\nWPd/6FDuerhi5Uo07l+L9j2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h1wHJhdddduZonHvSyNIKSxUT7+AgGa+0IqXYG+jV554I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tEJeZneVmjeC8J3z6awpbB5AV6f3pJ7CMIiz0F6GHtKq81nefHG/ZizQuwR6tPWXsTj/HHd0C4v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\nAHpomnYNIeQ0r/OS8D6A1zRNq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      "text/plain": [
       "<matplotlib.figure.Figure at 0x298b3abb400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x298b4ed4b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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RAiHKoPLFwKPuvgO4FjjBzH7o7iUtLdLMZgDfAU6PGgbpptt77wX3BixYAOvX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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x298b3aa53c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy.stats as ss\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "\n",
    "def riskmetrics_volatilities(parameters, data, sigma2):\n",
    "        alpha = parameters[0]\n",
    "        beta = parameters[1]\n",
    "        T=len(data)\n",
    "\n",
    "        for t in range(1,T):\n",
    "            sigma2[t]=(alpha*data[t-1]**2+beta*sigma2[t-1]) #yyf v2\n",
    "        \n",
    "        return np.copy(sigma2)\n",
    "\n",
    "def garch_volatilities_v1(parameters, data, sigma2):\n",
    "        alpha = parameters[0]\n",
    "        beta = parameters[1]\n",
    "        T=len(data)\n",
    "\n",
    "        # Data and Sigma2 are assumed as T by 1 vectors\n",
    "        omega=sigma2[0]*(1-alpha-beta)\n",
    "        for t in range(1,T):\n",
    "            sigma2[t]=omega+(alpha*data[t-1]**2+beta*sigma2[t-1]) #yyf v2\n",
    "        \n",
    "        return np.copy(sigma2)\n",
    "\n",
    "def garch_volatilities_v2(parameters, data, sigma2):\n",
    "        mu = parameters[0]\n",
    "        alpha = parameters[1]\n",
    "        beta = parameters[2]\n",
    "        \n",
    "        #T = np.size(data,0)\n",
    "        T=len(data)\n",
    "        eps = data - mu #kevein v1\n",
    "\n",
    "        # Data and Sigma2 are assumed as T by 1 vectors\n",
    "        for t in range(1,T):\n",
    "            sigma2[t]=(alpha*eps[t-1]**2+beta*sigma2[t-1]) #kevein v1\n",
    "            \n",
    "        return np.copy(sigma2)\n",
    "\n",
    "def Ngarch_volatilities(parameters, data, sigma2):\n",
    "        alpha = parameters[0]\n",
    "        beta = parameters[1]\n",
    "        theta =parameters[2]\n",
    "        \n",
    "        T=len(data)\n",
    "        omega=sigma2[0]*(1-alpha*(1+theta**2)-beta)\n",
    "        for t in range(1,T):\n",
    "            eps= data[t-1]-theta*np.sqrt(sigma2[t-1])\n",
    "            sigma2[t]=omega+(alpha*eps**2+beta*sigma2[t-1])\n",
    "        \n",
    "        return np.copy(sigma2)\n",
    "\n",
    "def General_Ngarch_volatilities(parameters, data, sigma2):\n",
    "        alpha = parameters[0]\n",
    "        beta = parameters[1]\n",
    "        theta1 =parameters[2]\n",
    "        theta2 =parameters[3]\n",
    "        theta3 =parameters[4]\n",
    "        \n",
    "        T=len(data)\n",
    "        \n",
    "        omega=sigma2[0]*(1-alpha*(theta1-theta2*(1-theta1)**(2*theta3))-beta)\n",
    "        for t in range(1,T):\n",
    "            z=data[t-1]/np.sqrt(sigma2[t-1])\n",
    "            NIF= np.power(np.abs(z-theta1)-theta2*(z-theta1),2*theta3)\n",
    "            sigma2[t]=omega+(alpha*NIF*sigma2[t-1]+beta*sigma2[t-1])\n",
    "    \n",
    "        return np.copy(sigma2)\n",
    "\n",
    "def gjr_garch_volatilities(parameters, data, sigma2):\n",
    "        mu = parameters[0]\n",
    "        omega = parameters[1]\n",
    "        alpha = parameters[2]\n",
    "        gamma = parameters[3]\n",
    "        beta = parameters[4]\n",
    "        \n",
    "        T = len(data)\n",
    "        #print('What is T=',T)\n",
    "        eps = data - mu\n",
    "        # Data and Sigma2 are T by 1 vectors\n",
    "        for t in range(1,T):\n",
    "            sigma2[t]=(omega+alpha*eps[t-1]**2+gamma*eps[t-1]**2 * (eps[t-1]<0)+beta*sigma2[t-1])\n",
    "            \n",
    "        \n",
    "        return np.copy(sigma2)\n",
    "\n",
    "def VaR_norm(vols, p):\n",
    "    T=len(vols)\n",
    "    invpdf=ss.norm.ppf(p)\n",
    "    VaR =-invpdf*vols\n",
    "    return VaR\n",
    "    \n",
    "    \n",
    "def mynormqqplot(data):\n",
    "    std_data=data.std()\n",
    "    s_rts = np.sort(data,axis=0)\n",
    "    len_s_rts=len(s_rts)\n",
    "    norm_quant_rts=np.zeros([len_s_rts,1])\n",
    "    for i in range(0,len_s_rts):\n",
    "        norm_quant_rts[i]=ss.norm.ppf((i+1.0-0.5)/len_s_rts)\n",
    "    \n",
    "    plt.figure()\n",
    "    plt.scatter(norm_quant_rts,s_rts)\n",
    "    \n",
    "    min_qt=np.min(norm_quant_rts)\n",
    "    min_s=np.min(s_rts)\n",
    "    min_ax =np.max([min_qt,min_s])\n",
    "    \n",
    "    max_qt=np.max(norm_quant_rts)\n",
    "    max_s=np.max(s_rts)\n",
    "    max_ax =np.min([max_qt,max_s])\n",
    "    \n",
    "    ax_x=np.linspace(min_ax,max_ax,len_s_rts)\n",
    "    ax_y=std_data*ax_x #std_data is like a slope\n",
    "    plt.plot(ax_x,ax_y,'-',color='r')\n",
    "    plt.grid(True)\n",
    "    plt.xlabel('theoretical quantiles')\n",
    "    plt.ylabel('sample quantiles')\n",
    "# mynormqqplot function END\n",
    "\n",
    "df = pd.read_excel(\"yyf_prices.xls\",parse_dates=[0])\n",
    "df.index=df.pop('Date')\n",
    "#df.hist(figsize=[10,10])\n",
    "\n",
    "df_log_rets = 100* np.log(df.dropna()/df.dropna().shift(1)).dropna()\n",
    "\n",
    "tmpdata= df_log_rets.SZINDEX\n",
    "\n",
    "# other possible data test\n",
    "#tmpdata= df_log_rets.EURUSD\n",
    "#tmpdata= df_log_rets.SP500\n",
    "#tmpdata= df_log_rets.SHINDEX\n",
    "#tmpdata= df_log_rets.SZINDEX\n",
    "\n",
    "mean_rts = tmpdata.mean()\n",
    "var_rts  = tmpdata.var()\n",
    "std_rts = tmpdata.std()\n",
    "\n",
    "T=tmpdata.count()\n",
    "\n",
    "sigma2 = np.ones(T)*(var_rts) #initialized volatilities\n",
    "analized=1 # or we should set to 252\n",
    "startingVals = np.array([mean_rts,.06,.94]) ##change\n",
    "\n",
    "#sigma2final = riskmetrics_volatilities(startingVals[1:],np.array(tmpdata), sigma2)\n",
    "#sigma2final = garch_volatilities_v1(startingVals[1:],np.array(tmpdata), sigma2)\n",
    "#sigma2final = garch_volatilities_v2(startingVals,np.array(tmpdata), sigma2)\n",
    "\n",
    "# start Ngarch\n",
    "#initial_vals = np.array([0.07,0.85,0.50])\n",
    "#sigma2final = Ngarch_volatilities(initial_vals,np.array(tmpdata), sigma2)\n",
    "# end Ngarch\n",
    "\n",
    "# start general Ngarch\n",
    "initial_vals = np.array([0.07,0.85,0.02,0.5,0.75])\n",
    "sigma2final = General_Ngarch_volatilities(initial_vals,np.array(tmpdata), sigma2)\n",
    "# end Ngarch\n",
    "\n",
    "#initial_vals = np.array([mean_rts,var_rts*.01,0.03,0.09,0.90])\n",
    "#sigma2final = gjr_garch_volatilities(initial_vals,np.array(tmpdata), sigma2)\n",
    "\n",
    "garch_vol=np.sqrt(analized*sigma2final)\n",
    "\n",
    "gr_vol = pd.DataFrame(garch_vol,index=tmpdata.index,columns=['Garch Volatilities'])\n",
    "\n",
    "gr_vol.plot(figsize=(12,7),grid=True)\n",
    "\n",
    "\n",
    "normalized_new_rts=np.asarray(tmpdata)/garch_vol\n",
    "\n",
    "gr_vol.loc[:,'Standerized Returns'] = normalized_new_rts\n",
    "gr_vol.loc[:,'Log Returns'] = tmpdata\n",
    "\n",
    "VaR = np.zeros(T) #initialized VaR\n",
    "p=0.01\n",
    "VaR = VaR_norm(garch_vol,p)\n",
    "gr_vol.loc[:,'VaR'] = VaR\n",
    "\n",
    "# call our function ‘mynormqqplot’\n",
    "#gr_vol\n",
    "\n",
    "ttmp=normalized_new_rts/(normalized_new_rts.std())\n",
    "# plot standerized returns using Garch 11\n",
    "plt.figure()\n",
    "mynormqqplot(normalized_new_rts)\n",
    "\n",
    "#calculate FOUR MOMENTS of standerized returns using Garch 11\n",
    "a1=ttmp.mean()\n",
    "a2=ttmp.std()\n",
    "a3=ss.skew(ttmp)\n",
    "a4 =ss.kurtosis(ttmp)\n",
    "\n",
    "std_rts=tmpdata.std()\n",
    "normalized_log_rts=np.asarray(tmpdata)/std_rts\n",
    "\n",
    "#calculate FOUR MOMENTS of original log returns\n",
    "b1=normalized_log_rts.mean()\n",
    "b2=normalized_log_rts.std()\n",
    "b3=ss.skew(normalized_log_rts)\n",
    "b4 =ss.kurtosis(normalized_log_rts)\n",
    "\n",
    "print('Type     ||','mean||','std||','skew||','kurt||')\n",
    "print('Original data:  ',b1,b2,b3,b4)\n",
    "print('After Garch:  ',a1,a2,a3,a4)\n",
    "\n",
    "gr_vol.to_csv(\"yyfdataout/garch_vols.csv\",index_label='date')\n",
    "gr_vol.to_excel(\"yyfdataout/garch_vols.xls\",index_label='date')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Garch Volatilities</th>\n",
       "      <th>Standerized Returns</th>\n",
       "      <th>Log Returns</th>\n",
       "      <th>VaR</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-05 00:00:00</th>\n",
       "      <td>1.856530</td>\n",
       "      <td>-0.166220</td>\n",
       "      <td>-0.308592</td>\n",
       "      <td>4.318935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-06 00:00:00</th>\n",
       "      <td>1.895925</td>\n",
       "      <td>2.495554</td>\n",
       "      <td>4.731384</td>\n",
       "      <td>4.410581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-07 00:00:00</th>\n",
       "      <td>2.007752</td>\n",
       "      <td>2.301200</td>\n",
       "      <td>4.620239</td>\n",
       "      <td>4.670728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-10 00:00:00</th>\n",
       "      <td>2.097463</td>\n",
       "      <td>1.149829</td>\n",
       "      <td>2.411723</td>\n",
       "      <td>4.879428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-11 00:00:00</th>\n",
       "      <td>2.121183</td>\n",
       "      <td>-2.527428</td>\n",
       "      <td>-5.361138</td>\n",
       "      <td>4.934610</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Garch Volatilities  Standerized Returns  Log Returns  \\\n",
       "Date                                                                        \n",
       "2000-01-05 00:00:00            1.856530            -0.166220    -0.308592   \n",
       "2000-01-06 00:00:00            1.895925             2.495554     4.731384   \n",
       "2000-01-07 00:00:00            2.007752             2.301200     4.620239   \n",
       "2000-01-10 00:00:00            2.097463             1.149829     2.411723   \n",
       "2000-01-11 00:00:00            2.121183            -2.527428    -5.361138   \n",
       "\n",
       "                          VaR  \n",
       "Date                           \n",
       "2000-01-05 00:00:00  4.318935  \n",
       "2000-01-06 00:00:00  4.410581  \n",
       "2000-01-07 00:00:00  4.670728  \n",
       "2000-01-10 00:00:00  4.879428  \n",
       "2000-01-11 00:00:00  4.934610  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gr_vol.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-2.3263478740408408"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ss.norm.ppf(0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00034467041816377546"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sigma2[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.09,  0.9 ])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "startingVals[1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Garch Volatilities</th>\n",
       "      <th>Standerized Returns</th>\n",
       "      <th>Log Returns</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-08-14 00:00:00</th>\n",
       "      <td>0.052824</td>\n",
       "      <td>0.415643</td>\n",
       "      <td>0.021956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-15 00:00:00</th>\n",
       "      <td>0.053162</td>\n",
       "      <td>0.071343</td>\n",
       "      <td>0.003793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-16 00:00:00</th>\n",
       "      <td>0.053068</td>\n",
       "      <td>0.073024</td>\n",
       "      <td>0.003875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-17 00:00:00</th>\n",
       "      <td>0.052984</td>\n",
       "      <td>0.094038</td>\n",
       "      <td>0.004983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-18 00:00:00</th>\n",
       "      <td>0.052917</td>\n",
       "      <td>-0.070457</td>\n",
       "      <td>-0.003728</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Garch Volatilities  Standerized Returns  Log Returns\n",
       "Date                                                                     \n",
       "2017-08-14 00:00:00            0.052824             0.415643     0.021956\n",
       "2017-08-15 00:00:00            0.053162             0.071343     0.003793\n",
       "2017-08-16 00:00:00            0.053068             0.073024     0.003875\n",
       "2017-08-17 00:00:00            0.052984             0.094038     0.004983\n",
       "2017-08-18 00:00:00            0.052917            -0.070457    -0.003728"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gr_vol.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0120120598058 1.0 -0.2775212690185174 2.927401571414615\n",
      "0.0124942989798 0.999877773024 -0.3805383841069472 5.1688877882752955\n"
     ]
    }
   ],
   "source": [
    "ttmp=normalized_new_rts/normalized_new_rts.std()\n",
    "a1=ttmp.mean()\n",
    "a2=ttmp.std()\n",
    "a3=ss.skew(ttmp)\n",
    "a4 =ss.kurtosis(ttmp)\n",
    "print(a1,a2,a3,a4)\n",
    "\n",
    "std_rts=tmpdata.std()\n",
    "normalized_log_rts=np.asarray(tmpdata)/std_rts\n",
    "\n",
    "b1=normalized_log_rts.mean()\n",
    "b2=normalized_log_rts.std()\n",
    "b3=ss.skew(normalized_log_rts)\n",
    "b4 =ss.kurtosis(normalized_log_rts)\n",
    "print(b1,b2,b3,b4)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2000-01-05 00:00:00    0.002352\n",
       "2000-01-06 00:00:00    0.037769\n",
       "2000-01-07 00:00:00    0.035341\n",
       "2000-01-10 00:00:00    0.018623\n",
       "2000-01-11 00:00:00   -0.043202\n",
       "Name: SHINDEX, dtype: float64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmpdata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.14251578,  2.41078127,  1.89478934, ..., -0.23284716,\n",
       "        1.11453178,  0.01484554])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalized_new_rts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.4107359417884724"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0.037769/0.015667"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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wRuwBMnrmWoqK9+zzuaLiPZXek11EyqdiTxbOeW+Gtm3rPW905Eh45x044QS/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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x27d7228c2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mynormqqplot(normalized_new_rts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.01650006,  0.01566656,  0.01865159, ...,  0.006359  ,\n",
       "        0.00605386,  0.00606919])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "garch_vol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tmpdata2=np.asarray(df_log_rets.SP500,order='F')\n",
    "tmp3=tmpdata2.T\n",
    "mynormqqplot(tmpdata2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "?np.asarray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4091"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_log_rets.SP500.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "len_s_rts=len(s_rts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "500"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len_s_rts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-3.2403591632986499"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ss.norm.ppf((2-0.5)/2513)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2.00207188],\n",
       "       [ 0.04420734],\n",
       "       [ 1.30273423],\n",
       "       [ 0.56905999],\n",
       "       [-2.77393438],\n",
       "       [-0.24909298],\n",
       "       [-0.87339187],\n",
       "       [-0.32416108],\n",
       "       [-0.75571403],\n",
       "       [-1.09388283]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmpdata[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(tmpdata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.13777424, -0.09469514, -0.09353656, ...,  0.06836638,\n",
       "        0.10245733,  0.10423558])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmpdata3=np.asarray(df_log_rets.SP500)\n",
    "np.sort(tmpdata3,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.13777424, -0.09469514, -0.09353656, ...,  0.06836638,\n",
       "        0.10245733,  0.10423558])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sort(tmpdata3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.037768650228951144"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmpdata10=df_log_rets.SHINDEX\n",
    "tmpdata10[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 下面开始演示参数估计"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\statsmodels\\compat\\pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
      "  from pandas.core import datetools\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.    (Exit mode 0)\n",
      "            Current function value: 7869.802372123189\n",
      "            Iterations: 19\n",
      "            Function evaluations: 89\n",
      "            Gradient evaluations: 19\n",
      "Initial Values= [ 0.4   0.96]   Initila Likilihood= 10600.6569201\n",
      "Estimated Values= [ 0.04725551  0.95274449] Estimated Likilihood= 7869.80237212\n",
      "Type          ||      mean      ||      std     ||       skew     ||      kurt      ||\n",
      "Original data:   0.014618164125 0.999877773024 -0.3480523513260165 3.9996646163169807\n",
      "After Garch:    0.00924431295491 1.0 -0.23455818354564506 2.8117975908206096\n"
     ]
    },
    {
     "data": {
      "image/png": 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fPh2bN2/Gzp07R7oro4bBwcFRtdnO5XKYPn36SHeDEEIIGTVUWQ5GsB9keGl74UAIkQSw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\n1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+E4BybhcbsQ9rnzJnOgNAbTjfKgZGD+9OpZ4mcZrSiQSuYgitue3aL6rpLB9QQgKjXV\nThkiLgun0TlQRo7aExPTlLtfw3f+JY+e6TUzG1cVVi0jg6BRca5ekzcevIyaQ0f36MJD+pjMRqNK\ndfpN6CHO85g8NILvXiYUNS/f3YiXNh+XH7fY18I69LIogEMrKkaw7jmr6262sCVBkfzF67uT3kZb\nz4BsnrrhQ2oD2yzm1MgV79p7B1ERlsYOWm7Z53bhgTf34Nl1R6EJhWN+N9VETunM98fYtB9lNF6r\n631b4hoYZU4CXrkZ9OR7RzTW1AadDZ45skx0oNo1pKXtQjzOF5VSEUHI55bR2k51pK7w5UAOxzmw\ngNKgtQ6/2YTSGEw3RpYLA/T9H5uFTy0aIy6nX+DSoGCEmnWwYtSA29YzgNcpmdDOxERy1ZyRTMm9\n2TXlKAt6MZSaOCIJbuq/t5wwtX8rmPjdV+X7//GbohH63Aa5brde6cC46jD23HsJLqcyCIQ7b9S8\nKxLQoxUpCpIph8DFSZOwi+NEqVWzRW4lAW/SzoHbxcnUm17bLpeCTaVhmV4WBcgZe9BBhvHSrUvl\nmU4+d5SrrpwzEgBk45ZVzPnxW9h6rA3VET/u/Z+ZuGD6sKS3pYxIf2rRGBnH/56XJPnPlu4BPLRi\nv6owmc6cSn1qhO1eSB0bq0Yo6FMPQspAjIehxgQAbYnsi9HQQfdGUYLneVMUL/qYSgIelCeyon94\n+6Dhb1NBjOeLjlIECIEw2pnccNhY7cqBNTjOgQWUBb2mi2lzAhkcMx74xBz85KoZuE4h6UYbq2Lm\nwOQ1pNUwlB10CUUo4vega0CtnCHoYcu3R7ipaw60mNp/qvjc4+vFz3sbJAlccqxaBq/f45YVToqZ\ng7i+VRvxezAY45nKPzy0aUUcx4nOgosDFiekGc1GpAT1EuvvRZwX9l1J0ZreO9iiWMd6w7Jr5gl1\nG209Gg3hoH/9HRQ25owulxUOD+QQX3lJQj2o1GIDMhZGlgfwmRSyBkoc+OlluPPiqcw6ACVoyiDt\npCsdc1oRiYwhYYr6xcocsFSRWGjtMZs50HYOfrNsH6bd87qhwl0szmNIiR8/vXoWvnvZGShPjN+k\nC326EI/zRaVURLBqXzO2HGvD9BFCYMlpTms/ivCxSh5Wm3hlE5l+VyrCPnx28SNcAg0AACAASURB\nVDgxIkSa5dCDasjnhtvFWaAVCf973fLBvSzoFZvaRPwe8LxaU5rn1ZOCl4owpTKYbD7aKlNn0trW\nO5TedyvFITaqHQDkdQDKCLjW78I+7bbyPC/PJNFqRcLfwiTNgcPvPj0PL39tKVMikIWGjj7U7Wky\nXpFxTC5Oyiix1xH+t5I5//k1s3Dj0nHo6IsyVUTiiefK8Q2KF0qDMFeeBVLDk0wmTgk7tgEAa7/z\nEbxx27lwuzhwHKercETg97jwwCdmA4Csa7tSYIC+D2SuCFLjjp9huHf1q+cPQi8lkrIA8Ni7QsG0\nkfGsJ+f6YoLmqKXGRxCL8/C4OCGz4veoxtd0IRYvzswBAQmEddj0rDuQ4DgHFiDQivLjIUy162eq\nuOvSafjo7JGyAluO41Aa8JguSCZRY2XhMR1tIhFvZWQnrlDnUSKVXgdX/24NLn9olfh3vwm5uh4q\nNW2msRe9TSs1BwA7Pa/kVSujf2Qy4zhhO2fWmO+u3ZBQ97DSXO7Y6R7U7WlK6KZrT6QSl9f80+xx\nuzCyTIgMs2owiLKFkzkoXijpd7n0JJQGpHmmvXcQ+xuTa7xpV23V8LKArNjay6Dx3LRUrhzncXFi\n9JyWZpUyB+or/qs3zUlAs4IfgQQVchiDjmX0nt+0dJwsQ0HXMNANt/QQ43m5DHSG6gBiPF90xciA\nVNdI3pNfvrEH4+56JZuHVHBwnAMLKA168odWlOGCZCWGlgTw0CfnqqLPZRbqNsSiY4VzwFIgUjsH\n7OLDr50/CUDqKhKNnf2iMWxGrq6dNUHq3J8F46S+ASRSZtQQTOtaCD+WG0ARxX0pFWlF1h+a+66e\nCQCY/v030GiyMOymJ9YjGhcoUEpH5WR7LwDgf/++Gdf/cS0A689yWKenxaajAj9VWYjooHjwwYmO\nbB+CJuh55pN/WosLHngnqe1kokMvIETr77niDNkyl4sTuzC3UWOtVHMgrfvy15bKfktnYlmvPYvC\nSDIMSgU0wNhQ5zhOplhEBznMiljoFQank/ISj/NFGeSYOIQUfMufhXT3lSgmOLOjBeRbzUGuFNnR\nKLVwDcmYSqtjAEA/FYWPaETLlbr+BDNHCRzFO57favaQNfH3dUKh8ecfW2e47uEWqWsyn6iH0FPL\nIWpLALDrpBA5NGoIRrIorGi5UHMg/U4ZbRJpRUk8MiMo6cKdJ80ZXUdaBIeqtWdQFYkkOuP/2XpC\nLDy3OgFWJp6ZIy3qbtURv/Dd+OqIpW06KByMrpA3K8sl+2pUeVDsBE/eJzM67i9tPo4PjksNvNLZ\nCf5zi6VahqWTqsFxnNgIDlBmDihaEaNsis5Sbq9vR5znMbIsgLsXBXDVnFG49fyJ+PyScaiOCM7G\nYExtbPsTmQMPY9BnLVOCiGhUhn3o6pdq2EjmwIgxEE3QimjceYnQINJMZjlZxPnMZSlyCWSuUzoD\njqSpfXCcAwsoDXjR2R9lNjzJNfAqRencAJ0yNwJRK1LKld64dJz4WTtzwFZrIr0W3k+yUIyOAhEV\niwHGZKUEHeGIM+ohlKBrDpbtbBD2nfhb66dhncyBUHOgjZKEwZxMFIpWL1E2eNNCMDHp9jKim6zj\nt3pUpCblRCILQSMaj8PFFeek6kDAo59bIDNmcymQMqTEr+pz0NTZL2a8tHDbc1twxcOrddexCx+d\nM0r8HGBE7XlAVOxpl2UOEtC43LtOdiDOAxfNGI5JFUL9xbcvnoYffnQG1n/vAs3jmTFSiPyXhViZ\nA2Mz53/mjsL++y7Fl86dgDgvFT2TsU0v0/zq9pPo6IsyAi7WpLuTgZLOVCwo1aCiptMhLjY4zoEF\nEMMyGWWWTCPTfQ7MojToMU3p2XtKiJhXKGhFt180VfysVXMgFCQz9p9ioRjtFxLn4MyaMo21JdCZ\njbiBoQ6wDVcjfX6tLAqgVisCgGvn1+DuywU6AJk/k7GXaVlUZWMkLYhcXkaWg1VIaVWRo0yn4d5A\nLK4pf+igODBpaATP3vIh8e9cGitLKJlGQn37+t8342O/W4MTbWpnV4lMnAudeSER83LKOegdiInv\nIF1zAI3s57RETYPf62IqzQHSuPfFc8arvrvv6pl48qaFOGNEqeo7M5kDQKhVIob2j17eAUBSuFNm\nu1u7B7DjRDs+/Ks6fPWZTXhnb5OqMLhEZzy2CwKdKW2bz1lENIri7SrCd+A4B5agZ3DkGljGYC6g\nNGCeVnTnC9sAaDc6A7QNYq2ag9EVIfHz23ub8K1/WKMXReNSGpNEveloOZFIvHruKNnvaOeFh7kI\n/Y4fXYwrZ48UpRYllSP2+tURQbu9uUutrMHz6mDdrz4+G184ZwIAYNF4QbHDSiEyAck6AMAHx83R\nikjmgJVoYKWGrUZ2SdSO5chHYzy8Ttag6OF1u/CNj0wGIO9wm21EEk0FY3FeHN82HBGyBqx3W4lM\nKNUNLQ2gdqrQYJJExunxtr13UHTAH3hrL77w5Hr0DEQ1s58vfGUJPC4O+xq6mEpzBIfvvxzfu3y6\nannA68Z5U4aI9Ei6wNhKhpCM5f/YWI/egZgYxKADWpuPtmLuT97C5Q+txsFmibaozBzo1oDZhGJV\nK4poyOk6mQP74DgHFkCKNvOlEVoupcoJkmkkR2cOnr55kew7rQGY59n6z2UhL4aV+hH0uvG5x9bh\nnxvrLdHE6HV7B4lzIH1/5eyRePzGBbjl3AnisnljymURDa3ImBJhvwdVYZ84MZF9a0XCKkJeeN2c\nqB5EgwevW+Pw0dkjsf57F2D2aOvOQdivrROuhZBP+zesugWr85/P44JfQxIy6mQOHCRAePHpjO5a\nxZRhJYjFeew80aESdCAF9n9+5yAeWr7PUGIznRhfLRSFEt43XReh5Nkv29WIp947otmAMOz3YFhp\nACfaeg2V5vQwbXgpfn7NLPz+M/PEZWYzB4DckXj/UIs4ttPOwb6GLuZvlfshjsotT200vX+rKFa1\nIq/bxRSUYMncOkgOzgxpAVLmIPcfwFxtClIa8KBvMM5s1KUFZTdf1ndsKVP2oHnR9OEIeF3iYG7l\nftIRxr5BYQKkr/WQEj/OnzoUQ0qkDqyRRK0KgV5kTInSoFeMIhLnQItDyyVkQVkDJCtzQMPl4mTH\nbAXKKM7h5m789NVdzB4DBIFEV1LSjZnGX1YfwlPvHZYt03NstFCiUd8yEOOZcowOig9lDF58tjE7\nkb3bfapD5UQ3JTIH9726Cw+8tRcL7luW8eMjKBXlj+W9bbTQ2TeoS42sivjQ0j2gO3abwXULxmBM\npZQhtpI5oGmnXf1RxBL1ZHRAK6QRDFFlDhLj4imTCm7JQE8lqdDBksB2aEX2wZkhLaA0AxPJgaYu\nS4azFnKVVlQm1m3ov8S0wU1H9ZSTit/jhtfNsaVMNbYd9gtpe1LAa+V+xqjiYyJ5F+d5zB9bgT9+\ndj4+v2QcACnbEfa5URLwoIuaXN4/2CJmHYxArldXX1R0TPQiYae7B/D02qOq5XwaG1/4PfLJ8mt/\n34Q/vXMQu09p67MTPXIiYzhjpJwr/P1/75D9ncyhlwY9zCxVNBZXNdZzUJwQAz45lA0m70ZjZ7+Y\nGSWO+5FmtfqWFq47a7TxSingS+dNwJfPm4hPJ5R+jBzu7v6YRCtifF8V9qG5qx99g/GU59hxVWHx\ns8fCu047OKv3NeO5DYIiHR1A0sp6KnejxYu3Ey9tOSEqvxUbWJ26HefAPjjOgQWk2zlo6xnAR379\nNr7/0g7jlQ2Qo4kD8RoaRetp+k45pUDBipJE/B61lCm0o/PjqkKIxnnxPlq5n3TmgGw+HgfcHIeL\nZwwXj8/t4vCrj8/GS7cuRUnCGSHYWt8Os6Ajm7FEvUOykaJM0czIvaAlDGn8v5X7sXx3AyYOCWPa\ncMEpePrmRXjmC4twwRlDmb9JJpJY4vfglW0ncUzRh2IwFncyBw4A0CITuWNUkHqcZ9YeQShhYJJg\nybsHmvGVp41pKjNGluLn156ZvoMEEPJ5cNel00S1omnDS1SZwCVU9+Gegahuj5fKsB87Ev0n6O7y\nyYCO4ptRKyKg6YbPrj8mfl62qxG/fnOP7vY8iuVmO8w7SA6k4Jt2wpyaA/vgzJAWQGoO0jWREP7o\nmoPNtmwvGSpGulEqyrsZ60YTXEXJ5rHs4rDfg3cPSNdsMBbHf7aekBWL0RhTFZL9bc05kLi0pDBd\nq4bg2vk1mDysBHsbOtHY2Y/n1x/DjY8b90SgQTsH0Zhx5uDGpePg87hw8xPr8e5+6ZrwJusc7EB9\nq6CocrtGL4lfvrEHfYNxmcFfEfZh6aRqVTdsgmSO/VjiOB5MKCid7h7Av7ccR99g3GmA5gCANB7l\nEq2I4ER7H4KJ55SMl2sPnsZrH5ySrff6B6dw6zObZMt2ZKHJW1XEj733Xipb9tRNC8XPr31wCk++\ndxgAO1BBR4K/ccFk247LSs0BALx714eZyx9esR8ANOmSSp9hSIkfPo9LFKlwYC8IrYgWLDFbOxSL\n81mt18kHODOkBYR9HnBc+lLQhJcet6FnSo4mDlAaNKby9A3GMO2e18W/6Ug5y+Gpb+3FwaZusR/A\nqXZ9jqdS/ciKYXC4WYhCu6jnwKiGgEQn73xhG1busRYRIw6pkDkgNQfa+6oI+TAQjWP57kZZhDGN\nrCIAwKtfPwf3/o/QKfmscYJ+PClY1ALrPMoZHU4BJEW1I42RSIHzLU9twDee3YJjrT0yNRMHxYtc\nryPbekzIMup1fv3y0xvxyvaTOUMjvXruKNxzhaAoREfiO/uiEgWGcay0c0C6K9sBPfEDFoyM+X2N\nbLokazybU1OO4229uvVXqWBMZUiljFcsIBkDmlnwwqZ6U799eMU+LLhvmaGtUMxwnAMLcLk41FQE\nxY61doOkjo+39WKXyU6zOhvLQa0iKnOgMxm3UnSU/7tkmuw7vSDQgaYu1e9ZSMU56EsYqTUVIfF3\ncQ1lJIIvnTvR9PaVII7F917aLtUc6HBo6UZEQWpSTHffi+kjS0WNcUIdMIrOsxy9ckYTIyC5bB25\nFkQ5ZXuie2xzV79IhXBQ3CDPSK49Dx+eJtDrrBSz5gqV9MHr5uDms6VeBD4GhY81FtH3wGq0n4Wa\nRC8GPSlsq+juj+Knr+5mfneoSZ2pXndYaLZ53ER/imQQixdnEzRAciYjfo8415w0aeyv2d8CADjc\nYr6Gp9jgOAcWMa4qbEprOhnQwYVLf7tK/NzarW/sspCrBcmlJgoAad1m0miMNMnRGwhZknosVIaS\ndw5IQfLYqhCOt/YiGosbqmsoC6c4DqJGuBGGkkLElh5DtSJAyswAcjlBHuyO0XaCGPaku+vp7gG8\nvbcJP3x5B1M9izX/l2nQipLhkhJjgzii5Ho0dDjOgQMBQZ8bP716Fv5680LjlTOI802ODwC78PXh\nT86183BSwt77LsUlM4bLlrFGolmjpGaSVoqItfDkTQvxf5dMkwVMzGI21diylBq/D+kUhLcw5ulf\nfXw2gPTR1mJx3hZHKh9B1KDcLg4b7r4Qc8eUm84SkU7asgZ9DmRwnAOLKA/50vaiK1OP8TiPk+29\nmPuTt/CHtw9Y3l4uDhlS5kDb2GMV/ZIJUKmfDQATEvQVogDEWoeGUuO+wUJ0jhzbqPKgWNSs1XCN\nQNm1k+dhunFNVcSPEWUBXHHmCFNqRTRnnzRpG3fXK2jo6LdFBUsPhBJEMjcHm7rxucfW4Yk1h5nv\nDCsNr6T7VCQGcVb00QjkN6xJ26EVOSD41KIxGFulT4HLNFgyjVqgHecvnzcRu39yCa6cPTIdh5U0\nhpXKZZJZ4yVRaQLUxb3JYOKQCL5SOzGp2rvnvrRY/FxBZR6ueHi15m++dJ46Q0woSumirUXjPNxF\nqrxG3hGeF+yDeWMqTNs85WL3buuB12KB4xxYRHnQm7YHSklLPNXRJ6bJnt9wjPELbeRKilmJgNcF\nn9ulmzmIUnKhxCkgqh3dA2qn4tlbPgRAyioMxqyd/FPvHTG9LumgSVLVn/jje9hyrE2X7uR2cZg0\nNCJbZqVxzbDSABo7+02pFVVRE5nSSTqcZsk7EqFr7hLejy7qXrEiNCyHgXaQR5QF8J//PRs/v2YW\nPjavxvLxfD3R/ZZkX2inKmiRh+zAQSbBm6gaY9X0hHzunMyKlSqi96wRjNCAAHsyB6mAvoa04hIL\nC8dVAmDXNqS7j0YsHi/KDsmApFZEgoKlAS+6B2KIGjAHAMnha8vRWqNcgOMcWER5yCtEi9NQYKSk\nXqze3ywOKlZTh0YdcbMFjuME/Xmdl5IoAk0aGsFnF48FIEWQWYV5Q0sDqAr7cDrhtA0aZA6UMJIG\n/ceGY7j8oVWJYxPuUVVEMDgPJHimRrzPIRF55MzKgD5ndDm217eLxr7es1ChoOXQz5QR3SpVKDMy\n9OPMit6z9LmnJOhjAPD0FxahpiKE6xaMSUq+9ezJ1bjurNFS9omiB+xvZHc5deAgF0C/qjRtaHx1\nGIvGV+Ktb56Lb100VfW7XGWYlCoyIazhL+z3iAZfLlFlfnDlDF2a1r1Xz0TQ68bls0aoviP0lXTS\nioq3CVoiYJjInJUlKLUvbj6OqXe/Ji5noSzo0IqM4DgHFlEW9CLOQ9bx1i4o/Y07/7kN/YkuvKQb\nr1kYdcTNJkI+D/boNMgi3PpvfGSy2GDrh1fOwC3nTsBHprF18CvDPpxORKwHEjPr67edo7mPaZQR\nymqmQuPb/9yGHSc6sPloK/oSVJ0qRZGbkeGt7D5sZUAfWxVC72AM33h2CwB10zEayuK7Aeq4sqnl\nbrZuZs7ocrF2oc9kozg9lIW84gRAO06kONmBg1zEnNHl4udvXyw5ASu/VYvnvrQYk4eVMDv1WslI\nZhLKjK9W/VM1yfLlUB+SgNeNiUPkmd9HPiU5C1OGlWDXTy7B6MqQ8qeiEXrXv7aLNE87UdzOgbxH\nCclO3fvKLvRH49jboG1jEFppe69DK9JC7ryBeQLC6W5Pg8cZZ3CByLKjp61TQnIwcQBAOJcNR1o1\ndYZJdJ7uYlsR9uG7l52hOWlUhn043T2Aw83dOJ7Qt9drdPXaNyTHgZYJZYFkLa7+3Rrc+cI2AEBV\nRG6EN3boF6krnQMr90ZZUKdsNERDmdqmC+gyoeX+k4ScqRKnKSoeOXeSjlfiL59bgGnDSzChOsL8\n3grKgl70R+PoG4zJsk53XqKOujpwkCugaYhKvj4BK0Oaq8o1J9vkdV1ah3nO5GoAQmf5bOPpmxfh\nb19cBEAu9ACox2Qt0OeRDiGT7oFYzmaL0g1yTwhFmdwTki3X66VEZvvTSYi9FAsc58AixEKWNHic\nLOcgmiR9KVdrDmhoFQKbUeVRoiriQ0t3P2p/VYf7Xt0FQD8jQFOueB7o1KmBiDC2UxWWT9gNnfpF\nzUrn4IQFaTsrzoGSSvbZv0hN1zLhHCiVoAhW75Masrk5DreePxHPf3kxc935Yyvw+m3n2lIXQK5d\nU2e/LLtz5Zm5VbDpwIEWqiNs56B2qjqLmquG4h0XTzG13tc/Mhl/+Mx8VY1WNnD25GosmSg4K8qa\nCSV9Uwv0eGw1c/vq9pP4y+pDmt9vOdYGAHhyjfmauUICuQfKzAGB3nxHHAjHOdCG4xxYRHkaJbBY\nBn2M6oimF91WbQtA7hKLBGjVHZhR5VGiMuwT+f8E5RYa6bTq3M+IXx0lqgjLlxk9D2SCJ6dkRZrT\ninMAyB2RTHeBrNDoU0B6UABAzED61U6Q3Xz92c0yR9voGjpwkG2cMaIUJQGPpnPg87hw+4VyoztX\nMwdDSwKyv7UOszrixyUzh+dcvVzEJw8QzRxVhl9/fDZe/tpSw9+SegWrmYOvPrMJP/nvTs3vDyey\nwgNpriXLVSh74ijnSTMKUaxaOAcCnBnSIkTnIA1RWFbmoKtf4inqRbeV4Hk+Z2lFBM0aL6YZVR4l\nKhWR/OGlAUMDkO41oKdAxcpAeN0uS/KalQlngpxTd795/qmSHmW03/fu+jCzj8Jls4Yz1rYX5RoR\nNcLx5HnesKO0nSBUos1H22Q0DD3KmQMHuYBXv342tv3gIpGHz4LSIMo1o5rGhrsvED+nu+eK3WDV\nclwzvwZn1pQz1paDqErd8Ng6bDraannffYMxvLu/WSWCQsawP3xmvuVtFgKU2RtS9E7m/WOtxlTs\nli7HOdCCM0NaBGnr/qKJNt3H23pxx/NbcelvV+G+V6QIwA9f3oFXt59Urc9KDPzqjT3i50dXaacY\n8wlvffNcAECLRiSFSJlayhwoogjKmgAWnrhxIf711SUAtCP/r2w7iXWHTquWuznOsJCZBnluiFFs\nJXNANwcC5LUYLHjcLtxyzgTZsh99dAYe/uQ80/tMFsqMCgHhhRL/N1POwUVU8yVaRMBssxwHDrIF\njuPAcZwu/75CIUCQq7QiQE6PymEfRhMXTR8GAPjY3FGWfkcrNa3Z36y53vV/ei/Rk0ZOUX3qvcP4\n9KPv44k1h2XLY4nBdNLQ3OrRkSmQMfwTZwky18RRJiIuT645rKlYROahTFBt8xWOc2AR5AFcuadJ\nVyoLAJ5ddxQvbKrHrpMd+DNl2D+x5jC++swm1fqszAH98D6ycr+lY83V8XfCkAhcnDaVR6o5sOAc\nKFLvZtUuSPRBq4bk1r+p7xMgZBNo5+CGhOSqFv5/e+cdJkdxLfpf7WzSaoM2SKuckBBIgAIiCAys\nMCIZg0GYYBsDhovBYGy4ti9cPxOMMQYHMI9szMfF5mKCwYZnQEY2MgKRQUJIllBCcZWlDVptnHp/\nVPdMT0/3hN3ZCbvn93361NNd1V1ztnqqTtUJYWcp87koCbMW96pVIquDx0yo4dJjx4Y+DygIpCWq\nhZ8trp30zh7Q0jWJGTFoAIePqQx9PuHAway47dSsjAUvCF7Eet/HuCLk5ErkmtxoZST3XjidRTec\nyG/On5ZUPec48fGGvb7l3llrFqHcidaWbWkEIk0zIWw3n62mZL2NUooVt53KL845DDA5lAoCKrQI\n1NoR5NqnPvas68wj4hUeXRDlIGmcpiorY4TKguRtDO2X/fyZo7j7/KnJNy7iXtm7OhPIU1QMKPAN\nbxnyOUgiEY47tGiiHtmhrL77kltByA/khRyVbz1zCj89yztKj409QJQV5zN3xkgeu+SIpJ63+KY5\nSZWHSJmkK8Sh36TbVnJtBTidIRed8eC7gloUA6HP4DY56k4m8XRy0sHGiToXJ7TFBQGGDxoQv6AL\np3LwjxXb45bf0dRGZ1cwlPPhr4u3ANE+h/bnXJRlqiguCITGEqVUlJndP1ZsZ+mm6LDVzulBbwSX\n6Qtk9y9JlnPOA4tiXo+XAt4dG99+97/1hXGcPT35jLBustmus9KRtMxNd6IVueP7b4sTWtSmfEAB\nSnUvjXqZ5aicyES3emAhZUX5/PhLB/Pr86ZyiMtUKB5+tvyxcJocZHpBcX9HF/vaOrH969M5oDlN\nnXo7EZwgpBN3dLBsnyfe97UZLPzR7KzNx9AbuHex2zrj+5vd8cqKqOg7blNUe76QK7tF6aCkMNrU\n96010aZcTjVLEqF5I8pBL9HS3sn2OOEt3ZFkgjFMLs6ePiLKOz8WmsRWzjNFVUlh/J2DJH703DsH\nW33CpLqxdzHe/9zbUWxEjJUie0UokWzH+YE8lt56CufM6LnSlyhOmWTDAHLrS8tCfTydC5zOqFXd\nDQ0sCJmkxseHyh3uN9lkmemmuCDgmSysP5HILvXfl2+N8mmrLY+M+GQ7KGe7QphOvBSveMnnEk3Q\nmSirtzdx6j1vpPy+6UaUgx7ijiBgc+6DbzNv2baYdd0xdu1b2Tam9zpStteUFtKwv4NnPtjIQ/9a\nE7dd2WxWBNbOgc/Ls2yL2QZMZkLrdsxLhqHlxb7Rp8YP9nf2sjM0pkvOC380m/d+/MWEyzsjOKVz\npf7LU4fz5anD+eNlR0WcX7q50eFzkL72OJ3Tk/H1EIRs4bXrTmD+9cfHLZeKrOJC6ikuCP/u+I17\nTofthpaOKMXPHa0wvNCSxQN9mvEKX+71TjjNimKFMe8ODyxYw4qtTby2PPb8L9tJPNyK4ElTaycV\nHiv6y+sbo859urkhwpxkj8uURbt2DqY5wqTVlhejNfzoOZOh98oTDojZLk2WKwclBXyyyftH8p75\nq4Dkdg56EprykBEVMaNI+GHH9E8mxGxPSHbFrcphTpPOybgd19ud6G3mmEp0BsyKnH3jN+cl50wo\nCNlA5cDChBZA9otykJUMKAiEdnXc475NTWlhyE+xK6ijFh5tv623Vu+kIJCXkYWWbMcrcWaLx86B\n07Ji+ZYGTj0kdWG+bV+RZCISZiOyjNYNXvnecaHjRGLp2rijDUTvHES+7KOrSzh6fBXXnjghKgqM\n346Fk6z2OSgpZE9LR0gh8iJdKyKDBhSwpaHVc0vSK4KU7ZRuD9a7k3RmThfOPpMJP0Xn80dWDqC5\nrTPt0YrcDK0ojl9IEHKQgoDiy1Ml83c2cr0jKILfzoHWMGt8NQD72rtodGVUtiO+ff3Rdznv4bdD\nlgaiHITxClHtnndBZNTA+obETJATxfaVjBfNMtsR5aAbHDysnAe/bmLGX/jIO55ljp1QHXVu3c59\nfPOx90Kf3TZpf3jbpEF3vux/umIW1588KSp+fLzMfrEm3dnAoJJC2juDEStdLy+t543PdoQ+x3Po\nThW2Hbq9K+PELca/XH0sH/3ERA6ynaCz1bbQGbkhEwOIvYpzzowRbNqznxc+3kxnNxLcpYKpI5Nz\nABeEXOGtG05kwQ/qWHX76RwwuDTTzRE8uOjoMaEkcI++6Z2vKKg1lQMLOOFAk8BynZUB2ebttbuo\nbwjvxgaDmV1oyUbcfhkAi9bs8jW3G1k5IOVmRY+9Zf6+ze25rRyIWVE3seeMTT7a4YCCfA4eVs6I\nQQOY/29je/a7N9ayz7HF5e6UH1kxkL3mcRUuW7odTW0MjpE5U0NWB5O2Q+2DegAAIABJREFUTXL2\ntHSEIgy4cz94rdrHYlhFMfUNrVx67Fi+eviohOvZOwEvL63ntxdMj7hmt+HWM6cAZpJp+4QcMbYK\ngOMOrEmqnenCGSUjU6tL//7pqRTm5/H8R5sB2NlkFKl0Z3J9+tuzcn4lRxC8iBU0Qcge7LDZSzbu\npbG1IyI5GpixRinF1bMn8C/HIpmT+Q479peWmBCn4nMQxum4f9tZU/jJX5cBxpRrWEX4PbEXT6sG\nFtLQS6FMm1pze7zJ2p0DpdRjSqntSqlPfa4rpdS9SqnVSqlPlFK9n/7VQd0ko93PGO2dPj2oNYE8\nuOXMyZw1bTiF+Xm0uZJt+Nkeun80gKhIRX5bkyF0VusGodCce/a1s2pbExt3R5tnJRuPfq4VCeiU\nKUOZPLw84Xq2k2pHV7QyojUcNa6Ki48Zy8XHjI2Y1E4YUspnPzuNMw7L/q38TA0gAwpN8rXbvmLy\nQLy+cntG2lNcEKC61F+ZFgRB6E2cizWPvbkuajVba7OIUxkjKqHT1OiD9SbCXroXWrIZe6FxcFkR\nF80aGzrvFyFqkGXe3Bs0i3LQazwOnBrj+mnAROvfFcCDaWhTiJLCfKaNGsTAIu/Nl66gJqAUIytL\n+O0F0xlaXhwVRvGJt9dHeLQfNrKCwWVFng7Obp+DXfvix/HP5h8N+wdwb0sHc+5+g+Puej3i+sQh\npUnbh3//pIk8fukRHD0+2qQrFqMqw46+bnOseFGfCnMk+k2mu8LkYUZZ++W8lYBshQuC0H+5Z/4q\nJt/0Kut3hU2HurQmT3lH3rvupAMB2NUcvSgoOwdhzjtiFMMrinn+qmMAeOo/jgai8xjZw3xlSUG3\nchzF4osHmUR/e1raeWnJFsbe8LeU3j9dZO3MRmv9BrA7RpGzgCe04R1gkFJqWHpaZxhUUuCbQCOo\ndUSiF78cBbf9v+Wh46L8PCYO8bYZdWf+8/qRcJLdHgfhH0C/3ZPuZKLMD+RRN2lI0vW+OjOce6DB\nFdJUo/uEw1em+4O9w3bE2EoguxVXQRCE3iao4asPve34bBYUBw2InisMKTe7nnY0IyeiG4QZMWgA\ni278Yiiyn+2r6d4dsMfDyoGF7I0TGCVZ7Oh4u5rb+fNHm1J233STyz4HI4CNjs+brHP17oJKqSsw\nuwsMHjyYBQsWpKQBbU2t1O8Jet5v5679dAYJXdOt3h7xu5paQmV279nPgHzl276hJYqtLaYTf7R8\nFeM71/u2raWlhR3bW1P2Xd00Nzf36N5724yJ1buLl3ler9+xq9fa7sUpY/OZ93knL//zTYaXhnXm\nPXv3Uxgga+UYj7uOH8Br6zvo3LyMBfXL41foRcZV5LFhu/GrWbVyBQua4+frSJTelmN/QeSYGkSO\nPacvyvDw2gAfbgubE21vagt9x5aWVrZt28abC98IXbfHpV0bPgPgs41bo+755sKFFAb8NYS+KMdE\n2dNq5hnvLf6UgbtXhs6vW2cWJffUb6QzqPnbawsoCEBRCuS4Y6eZ623Z3UQ5++KUzl5yWTlIGK31\nI8AjAJMmTdJ1dXUpue+/mpaxdNcmvO734EqzIlBXNwuAV3Z+wtKdG6PK7esgVP83n75J9cBC6uqO\n9HzeD8s28Z/PLgGgrGYodXWH+bat+P3Xqa0dRF3ddN8yPWHBggWe3ztR2juDfP/1V/jjv713DgpL\nSqmrO87zWm9QMHIn8x59l/GTp0aYJd27/C0GFuVTV3dUjNrdp6dyTITzevXuifPH9R/w5uodgGby\n5IOpm566bNHpkGN/QOSYGkSOPacvyvDY44JM/PErEefs71j09j8YPqyGurqp8KoxRXngipNZsbWR\nA2vL+NUHr7Bid3QG7LoTTohp3toX5ZgorR1dXLfgVYaMGkdd3YTQ+cWdn8HqVcw89GCe+ewTrvmn\n8Xn8/Bdf8r1XonL83w0fwLZt7G3TDK2thfotPf4emSBrzYoSYDPgDEkz0jqXNqpKCmlu66S9M/qF\n7QrqCFvAROznO7t0KEauF3MPD0+m4jokk90OyfFs9b0Sl/QmdnZK97ZtAukkhAQZUl4USgTUF0y1\nBEEQkqEgkMcd5xzqec34HET+LgbyFFOGV8RM8plMstD+RnFBgNryIj7b1hRx3rYiKiuOXB9PZF4V\nD+eUYWcc8+9sJpeVgxeBb1pRi44GGrTWUSZFvYltN+92aHnuw0007O+IUA6GuZQDd/i5lvZOltc3\nJpysys/73ibL0xz4MsCKUDSuemBan2uHQNvZFKkcaGQimyoGO6IFiUwFQeiPXHjkaN658YtR54Oa\nCD9FN2dPH+F5PlYdAcbVDKR+r7dZ9yEjIvPfeOU6Shbn3KupNTsTpCZC1ioHSqmngLeBSUqpTUqp\ny5RSVyqlrrSKvAysBVYDvwO+k+422kmwdjuUg017WvjBs0tYtb05YgLkDqN4+XHjmDiklHJLc/3V\nPGNT+P7ne2I+c+3PT+fUKUN9HXltNDonnT4nDS3jj5cdxd0XTEvrcweVFJKnopPLaa0zHumnr2A7\n1YFE2BAEof8ytKKYi44eExGoRFvRisDk1bl41piIOs7fTyFxqgcWsXNf9KIfwKiqEg4aWhY6v2xL\nQ0L33N7Yyh0v/5vOrmirEa11yBIh1dmX00nW+hxorS+Mc10DV6epOZ7Y4UWdW1Gdjlj5zglQlSvD\ncWeX5kuHDeOe+avo6AqGEjTF29bKy1NUlRby6rJmOrqCvtuNOsvzHAC8fO1xnH7vwohzj1x0OEM8\nshz2NoE8RdXAoiizIjv2tNBzhjr+rqIbCILQn6myIuW88PEmJgwuMzsH1lhz8TFjo8p/46gxPPyv\ntWluZe5TNbAwel7lWN4/dEQFK7YasyN3tEI/bn1pOX9bWs/R46vZ1tjKSZNrQwqBxlgi7GxuY3tT\n/JDz2UrWKge5gL1z4DTx6QyGNckCh+e7O0/BvvZOqh3hPJNZFaix6m3c3cL4wd6hT4Gs1w6qXPGc\nSwoDGVEMbGpKC9nRFPkjEtQ628WYM9RGKAciVUEQ+i9b9u4H4LqnTZCRgoCK6Ys3qqqEt244MbSQ\nuGxLAw29lMCrL2ErYZ1dwVAiOk04909NWXjudcjwCo87RJNvze1WbG3izldXMO39jfzl6mPNvbWm\nIJAXUkpyJReSG1EOeoAdQ9dpVtTeGdZIC/PDGX6rB0ZO/k+ePJR1O02Yq9372kNa57UnTiAeM8aY\nWPGxTItywefAnfvh/q+lNcl1FDWlRVHJ5UwSNJnIpgKn301zW25njxQEQegJZ04bzrMfhuPgd3Rp\nDoi12Eekr+KBtWUxSgo2tj/h7pZ2hpSZMchpWVHtWKTc1pSYGZBtIbLbmi+scjg8Gz9F42O3e197\nzjqMi3LQA6pKCikM5LHBkeXQuXNQ6DD5cXrF2+Gy7C2s3c3tITMLZ8pvP+xdiHhOySrL17yLC8LK\n0/zrj2fCkMz+2NWUFrJ+wz6CwXACu6D4HKQM507RB+v3cM6M1IUyFQRByCWOHFcVda64IDdXmbMZ\ne+F1e2NbSDmA8KJfdWl4XNra0Gr5GXoP+mfe9yZlxfm8tXoXAL9buA6AfY7oikFrW2JwWRErtzXl\nrHIgPbEH5AfymDZqEB+uDzsRdzgcVNbsaA4de0UUsDvlk+9uoMuKmZmIo6atHNz01099y+SaI22m\nFQMwTuMbd+9n/H+/zLbG8ApCjr7bWYfzB3euKAaCIPRjivIDVLiyIeeqCUo2M2GI2Y1xzse0I+Do\ntFGVoeO2ziDbm9o8M1EDfLKpIaQYuHlwwRo+XL/bzL0I71jECkObzeRmq7OIEZUDIpxOOhwOyZ9s\n2htR9vnvHMO87x8f+jzB2kIMao1dLZDAjN72T9gSwxO+I6hztlNmiiEO28Njf/FPwPY5EO0g1Rwy\nojzTTRAEQcgog8sizY2bWsXcMtXYkSJf/TScXdppVjSuZiAvXfMF7pxr8k+c88AiZv5sPjpJ2+w7\nX13B3AdN8lulwn/bXA01K2ZFPaSypJDNe/fT2tFFcUEgYuegxhW+dMboyojPeXmKKcPLae3oImjt\nHMTIgRaiuCDguSXppK2jiyJZhUgKZ8zjzqBm4aodJlqRiDHlFIriKghCP2dwaRGrt4dXtBONliMk\njr0784pTOYAIy4pDR1YQtJSBzZaj+L72LkqLkp8i24rHwcPMAtiOHI1YJCN0D5kyvBytCTkX25EE\nrj1xAn++6pi49YeUFbG9qY0unbhZEZgfFb+tLzDbY0U5YL84c0xl/EJpYrgrMd1Fv39Pdg5SzLET\nqgFx8hYEQXBHKRxSJrkMUk0gT3HcxBqA0CKsmcBHjkHDBkVGStwVY34VC43JdP0F65m5iuwc9JCR\nlWZCuctKk73bchK+4MjRUZNNL4aUFfPplsaQz0GiIR6rSwvZ3thGS3snq7Y184d31nPX3MPIy1No\nrY1y4IiWlK08/e1ZSW/f9RZOxySbNTv2RSVGE7rP7y8+gsYczhopCIKQKga7rAv8siALPePkKUNZ\nuGonO5rbqC0vNj4HrqlWzcAiyovzabRMu3bta2dM9cDQ9Q2NXSRCV9D4e7r/trlG9i8tZzm2PZsd\nAnPl1kYKAspzounFkPIidjW3hRxgE905qCktormtk8k3zeM7T37Ecx9uCm2HtXUa06ZcMCsK5KlQ\n7OFMU+azhbhXYkmnjOKCQETECEEQhP6KbZf+rWPHsfr202RHtZcYYe0K2HMkiE4DlZenmOTIlry7\nOXJR8KZFkT6eN5x2kOezdu9rR6Fy/m+ZHbOyHMb2SLd3DlrauxhcWpTwqv3QimKCGp54ez2QmEMy\nRK5yl1s2dbZj9IbdLebeGUwolosopfivUw+S1RtBEASh17EXF/e0tGfNIllfxLbieHHxFnPCx1jB\nmUfinbXhqEReeXkuOWYsn956Cp/97LSIuVZ9Q2vWJ6BNBOmNPaS8uID8PBXaOehyxMhPBPcEPtG6\nzqRqLe2m4859cBEA/65vBCIdbIXEuKruAO4+fxq/vWBa6NxNZ0zOYIsEQRCEvoi9yBfLf1DoObZy\n8PiizwkGdZRDss3IypLQ8esrt4eOD7l5XlTZ/DxFaVE+hfl5ERYfTa2dId3gjR/OZv71x0fVzQVE\nOegheXmKqoGFoZ2DLq2TSnqRiF+CFzWOnYP1u1oirrV2GNs4Z+I1ITmckaXmTK7NYEsEQRCEvkiN\ntci3q1n82nqT8uJwPok9Le1WLoLoedqIyvB8zLkA64VTIXjkm4eHnJ4h7Ds6urokK3I4dQdRDlJA\ndWkRO62XuzPJnQM73FV3numF1ppOy7k5VzPzZQNDK8I7OuWuRDWCIAiC0FNGV5mV6hMPGpLhlvQf\ntje1mWhFHtMj52Lttib/PFIQGXFvyvAK7v/6DMe1nrcz04hykAIGlxXx2bYmuoKaYFAn7Ddgc7gV\nzvPZK2clXMfP4bmxtTOpbMuCN84Ecn6OyoIgCILQXSpKClhy88lcN+fATDelz2PPr7Y3tRmzIo8y\nU0eGTbHX72oJmWgnQllRfih/jygHAgAnT65lw+4WPt6wh86gTnpSfsKBgwGSSrjhN2Hd0dRGZ5e9\ncyB/3p7wy3MPC4WHFQRBEIRUUzGgQBby0kCtFSXPjgzpFU1oUEkhXztqdOjz2Q+8lfD9lQpHqewL\nuZFkSTQFzDrAJHbatGe/2TlI8kW/ZvYE5kyuTcrESClFZUkBe1xhNtft3Edn0IQyzQ/kfgfNJF+d\nOSrTTRAEQRAEoYfYCedWbm3i92+u8y3387MPZUhZEffMX0VrRzDq+vKfnkJHl3e4o+rSQuobWmXn\nQDDUloc10mQdksE4NXfH9+C9H5/EA5ad2+mHDgVg4+6WkM+BrEYIgiAIgtDfKS4IUFlSEFMxsJk2\nalDoeGdzW0Rwl5LCfCp8/BBtJ+Zcz3EAohykhNKifEoKA2xrbEs6lGlPKAjkcfqhw3j6iqO5+/xp\nFOXnUd+wn64ucUgWBEEQBEGwGVCQWP6puklhB/G7X/ss5Etw7ITqmPVqrEAxfWHmJcpBiqgtL+ax\nt9axcNXOUBirdHHU+GqK8gMMqyhmS0Or7BwIgiAIgiA4aPcxB/LinBkmGeqT726goyvI7FH5/OFb\nR8WsM6baRJ9q6+zqfiOzBFEOUkRteTi0aL0jRXc6GVYxgL99Us+bq3cSyMv99N2CIAiCIAip4BtH\nj45fyOKOcw4NHTe2djIgX8W1ChlbMxCAjbszMwdMJaIcpIhpo8JJs+obY8fH7S3G1hit9cP1e8Sk\nSBAEQRAEwcKZ3DQeRfmRJkhDSuLPqYZXREZEymVEOUgRzsx6OvGdq5QydWTYiaayxDsPgiAIgiAI\nQn/DOU87P4FohE9eHjYjKk4g+qMdnMY27c5lRDlIEcPKi+MX6mVKHLkPqgaKciAIgiAIggAwrCI8\nT7v5zMlxy9tmQgCBBGbLtVkwD0wVohykiGGDwp3ify+P7bTSW0x3hN9ankRmP0EQBEEQhL5MSWF4\nAdVtNuRFbVnYlzSRtFGF+X1nSt13vkmGGVYR3q46ZkJNRtowqqqEv193vHU8IE5pQRAEQRCE/kci\n0RzzHdsFycz7pzoWanMVyZCcIipLvJNipJsDa8v46CdzKC4QvU8QBEEQBMFmzuRalm1uSLj8jNGD\n+GjDXhKN8bL69tPSHs6+NxDlIEUopbj5y5M5dERFppsi/gaCIAiCIAguHvrG4XR0BRMuP9TyU2ju\nSKx8fiLOCTlA3/gWWcKlx45j5tiqTDdDEARBEARBcBHIUxQnmCkZ4IsH1QJQWZT7uwHJIDsHgiAI\ngiAIguBi7uEjOWJsFWuXvpfppqQV2TkQBEEQBEEQBA9GV5dkuglpR5QDQRAEQRAEQRAAUQ4EQRAE\nQRAEQbAQ5UAQBEEQBEEQBECUA0EQBEEQBEEQLEQ5EARBEARBEAQBEOVAEARBEARBEAQLUQ4EQRAE\nQRAEQQBEORAEQRAEQRAEwUKUA0EQBEEQBEEQAFEOBEEQBEEQBEGwEOVAEARBEARBEAQgy5UDpdSp\nSqmVSqnVSqkbPK7XKaUalFKLrX83ZaKdgiAIgiAIgtAXyM90A/xQSgWA+4E5wCbgfaXUi1rr5a6i\nC7XWZ6S9gYIgCIIgCILQx8jmnYMjgdVa67Va63bgT8BZGW6TIAiCIAiCIPRZlNY6023wRCl1LnCq\n1vpy6/NFwFFa62scZeqA5zE7C5uBH2itl3nc6wrgCoDBgwcf/swzz/T+F+jjNDc3U1pamulm5Dwi\nx9QgckwNIsfUIHLsOSLD1CByTA09kePs2bM/1FrPTHGTepWsNStKkI+A0VrrZqXU6cBfgInuQlrr\nR4BHAJRSTbNnz16Z3mb2SWqAnZluRB9A5JgaRI6pQeSYGkSOPUdkmBpEjqmhJ3Ick8qGpINsVg42\nA6Mcn0da50JorRsdxy8rpR5QStVorWP9AVfmmgaXjSilPhA59hyRY2oQOaYGkWNqEDn2HJFhahA5\npob+Jsds9jl4H5iolBqnlCoELgBedBZQSg1VSinr+EjM99mV9pYKgiAIgiAIQh8ga3cOtNadSqlr\ngHlAAHhMa71MKXWldf0h4FzgKqVUJ7AfuEBnqxOFIAiCIAiCIGQ5WascgDEVAl52nXvIcXwfcF+S\nt30kBU0TRI6pQuSYGkSOqUHkmBpEjj1HZJgaRI6poV/JMWujFQmCIAiCIAiCkF6y2edAEARBEARB\nEIQ0IsqBIAiCIAiCIAgGrbXvP0wo0deB5cAy4HuOa1XAa8Aq6/9Kx7UbgdXASuAUx/nDgaXWtXux\nzJo8nutZDjgek9ugEzg3RruLgKet+u8CYx3XuoDF1r8Xfeon/d3i1D/EIcdtwA67vtezHOd2Ai3W\ntVOse98E7APagM+BX8SQg58cr7TOLwbeBCb71L/eavMnwD+AMX1Ijs7nvw182g05XmK1wZbD5f1Y\njnsxyQhXAHOTlOPdDhl8Buzthhwvttq1Crg4R+W4FfNerwVeBWqSlONoqz0fWzI6vRtyvBP41Pp3\nfhrk+AawkPAY85Kj/rlezwEOAHYDQWCD6/n/15JhOybvjd8YczuwEWh2nfcdOxKRITCbcF9eDLQC\nX8nSvrjA6oedwO8c905mjPGTo28fc5XzHdMz0BdTLccbMYFS2oE1Vn8YkuQ73SM59pH+eLPVF1st\nOe4E7klGjo7rcwENzPSp7/n+Z1COaa0fca+YF2EYMMM6LsMM3JOtz3cBN1jHNwB3WseTgSWWkMdZ\nf8yAde094GhAAa8Ap/k817McMBY4DHiC2MrBd4CHrOMLgKcd15pjfefufrc49e8DZlj1l1p/uDlW\n/V+6n2XV/431rB8DD1llD8H8SJzseP7Cbsix3FHmTOBVn/qzgRLr+Ko+Jkf7+VcCTcRWDvzkeAlw\nXwJy6Oty/BlmUrYGE+TAb1Ib9/0HvouJTJawHDE/iGut/yut48oclON26352/VuS7I+PAFc5vtPn\nScrxS5gBJR8YiAknXd7LcrwdeNw6nomZBEy16u8BbvR4zt3Ag5h3d5Hr+S3AcVb9FuBLPjI4GjO+\nuSe1vmNHou+0o0wVRokpydK++BPgC8ALwIeO5yczxvjJMa58rGtj8RjTM9QXUy3HJcC/MGOs5/MT\neKd7JMc+0h/dz/8QOD4ZOVrXyjCLEe/grxzEff/TLMe01o+4V6yLHg/+KzDHOl4JDLOOh2GSi4HR\nTm501JkHzLLKrHCcvxB42OMZccsBjxNbOZgHzLKO8zGapq2JJzIZS+q7JVvflqNVf727rPXv51ZZ\n+9w8zIDofv7TwH90R46O868kIJPpwFuOzzkvR6AUs3PyJrDG5zv4ypEElYN+IMeNmAHc8/lJ9sdF\nWL8xicrRfS/gYeDCHJPjjzGra2Oscy8AVyTZHx8G/ss6ngUsSlKOPwR+4rj2e+C8NMtxOeExZh9w\nhke5ldbnS4DHHOd/Dmx3PGcJ8EKc7++e1PqOHYm+047zVwBPZvE7bZ/7HrAnxvM9x5hYckxEPq4y\njxOpHGRDX+yxHDGr4TP9nh/vne6pHPtgf1yI2YmIeifjyRG4B6N0LsBfOYj7/mdSjr1d3/kvYZ8D\npdRYTOd81zpVq7Wut463ArXW8QjMZMFmk3VuhHXsPu8m0XKxCLVBa90JNADV1rVipdRHSql3lFJf\n8amf7HdDKfWoUmpmAvX3E5bjJswqa71S6lHreq31r9x6ll1/E2Z1wPn87UAdZrvRSwa+clRKXa2U\nWoPRNK/1kYOTyzCauE1fkONtwK8xmbcLfL5DvP44Vym1VCn1nFJqFPHpa3K0kw7ehtnS/bVSyr6/\nk7jvtVJqDGZV458+cnDilKOvHFxksxzXY1YGl2JWvidhJkRuYsnxFuAbSqlNmBDQ3/Wo78YpxyXA\nqUqpEqVUDWbV0qtP95YcDwKGEB5j8oFi6/g2YLhH/RZH/QMwv4k2G33aH4tYY4cf7nfa5gLgKZ86\nGe+LjvoNQInP82ONMYniJ59YZLovplKO/4NREK61k7a6SHTO0x05OukL/bEEeFdbM1wXvnJUSs0A\nRmmt/+bz/Z33iPf+Z0qOvVLfj4TyHCilSoE/A9/XWje6r2uttVLK64+VjYzRWm9WSo0H/qmUWqq1\nXuNXONHvprW+PIH6BRit+Pta60bn74Rd3yqrfOqHUErlY360X9Zar43XPo923Q/cr5T6GvB/MDbb\nniilvoH5cTvBcTrX5TgaOEBrfZ1S6oJ47fLhJeAprXWbUurbmEHgRL/CfVSOecBIzIp/BSZh4a+A\ni+K10YMLgOe01l2xCvnIMSmyUI4BjHIwHfhvzET4RozJVqJciDHR+bVSahbwB6XUIVrroFdhtxy1\n1n9XSh2B+VvuwPjixPxbpEqO1hhzGvBHnzHmcqXUXL/bx3t+b+HXF5VSw4BDMSt0Mcl0X7Qvu+/b\n0zHGuke33tVM9kXrY6rk+HXr9/0JjNnPRRjTn6To6W9eX+iPFuMxO1kJo5TKw5hzXpJMPZ97ZUqO\nvVbfj7g7B0qpAoxi8KTW+nnHpW2WoGyB2Ss2m4nU8Eda5zZbxxHnlVIBpdRi699P/crFaePt9j3c\nbbB+4CqwVji11put/9ditpeme9wy2e8Wt74lx9nAcoccRwI7PZ61DWgERjnOjcQ4h9nPfwTjkPeI\nVbe7cvwT8BXrHm45opQ6CWPycKbWus0+3wfkeCQwUyn1OXAWMEwptSAZOWqtdzlk8ihm5by/ybES\ns3r7vHXuBWBGN/tjxIpMEnLsthx6Wj+FcjwCwFIMRwIvAsckKcfLgGes+7yNWXWvSbI/3q61nqa1\nnoMZsD9LhxwxY8z7loxsOjGOf+7nhOpjVhLt82swOw82o4CNHjKMhefYkYwMLc7DmDR1+Dwn433R\nIcMKzOqu+/nxxpiYeMnHS45+ZKovplKO9u87ZgX3JeDIZH8beypHi5zvj0qpqUAhxrQomTlPGcav\na4E13h8NvKiUmukhR9+5Y6bkmOb6YXRs+zWF0XKjPMOJdiq5yzqeQqTjw1r8HZL9omnELEd8n4Or\niXQqecY6rgSKrOMajJNMVKSe7ny3ePUtOT7hUf9XHmV/SaTj4sNW2UOtc3dYcvF8fjw5AhMdZb4M\nfOBTfzpmwJ3oOt9X5GjX30ByDsm2HIc5ypwNvNNP5fgM8DXr3KXAs8m+1xiTks+JYd8dQ45VwDpL\nnpXWcVWOyXEZUI9RMNdidgx+nWR/fAW4xDo+GNjiJc8YcgwA1dbxYZgoMflpkOOnGHtgd/29RDok\n3+Wsj1kJXOR6vtsh+Qy//mTVcfsceI4dicrQcf0dYHaM52ZDX7TPPQt85Hp+QmNMDDnGlI9H/ceJ\n9DnIVF9MtRyHO+o/B1yZ5DvdIzn2of5YhPHN2hOrP/rJ0VVmAf4+BzHf/0zIMZ31I+4Vp6N9AbO9\n8wnhEE52p63G2CGuAubjGIwxA98ajHOE01t8JuYlX4PxZvcLM+f5i8QPAAADZklEQVRZDrO6tgnj\nqLYLWOZTv9jqYKutzjLeOn8MxqZ3ifX/ZT71u/PdHrU7nEf90xxyrMdE5NhknbfLNmBs8qoc55wh\nD+3oBXda92ojHB7NL4Smnxx/i5mMLMaEG5viU38+ZiUvItRmH5Gj8/kXE1s58JPjHZYcl1hyPKif\nynG9dX6dVW50MnK0rt1CjJCJseRoXfsW5n1fDVyao3LcYd1jHWaVsTrJ/jgZeMvqT4uBk5Psj8UY\nh+DlmEFwWhrk+D6RY0y99W8lZpXOLrcZONFRfz/GzKTTumZH0XuAcCjTF/EfY+6y/lZB6/9bYo0d\nSfbFsVab8mL05Wzpix3Wv2ar/GSSG2P85OgrH1d9zzGdzPTFVMvxFsIhONdjxl3PCRn+73SP5NhH\n+qP9/HbiO8bHnWMSWznwff8zJMder+/3z+6AgiAIgiAIgiD0cyRDsiAIgiAIgiAIgCgHgiAIgiAI\ngiBYiHIgCIIgCIIgCAIgyoEgCIIgCIIgCBaiHAiCIAiCIAiCAIhyIAiC0G9RSnVZSYCWKaWWKKX+\nU5mMorHqjFUms7ogCILQBxHlQBAEof+yX5sstFOAOZg45DfHqTMWk/ROEARB6INIngNBEIR+ilKq\nWWtd6vg8HpOYrAYYA/wBGGhdvkZrvUgp9Q4mA/M64H+Ae4FfAHWYDJz3a60fTtuXEARBEFKKKAeC\nIAj9FLdyYJ3bC0wCmoCg1rpVKTUReEprPVMpVQf8QGt9hlX+CmCI1vpnSqkiTJbmr2qt16X1ywiC\nIAgpIT/TDRAEQRCykgLgPqXUNKALONCn3MnAYUqpc63PFcBEzM6CIAiCkGOIciAIgiAAIbOiLmA7\nxvdgGzAV45/W6lcN+K7Wel5aGikIgiD0KuKQLAiCIKCUGgw8BNynjb1pBVCvtQ4CFwEBq2gTUOao\nOg+4SilVYN3nQKXUQARBEIScRHYOBEEQ+i8DlFKLMSZEnRgH5N9Y1x4A/qyU+ibwKrDPOv8J0KWU\nWgI8DvwWE8HoI6WUAnYAX0nXFxAEQRBSizgkC4IgCIIgCIIAiFmRIAiCIAiCIAgWohwIgiAIgiAI\nggCIciAIgiAIgiAIgoUoB4IgCIIgCIIgAKIcCIIgCIIgCIJgIcqBIAiCIAiCIAiAKAeCIAiCIAiC\nIFj8f702QNV83RpaAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f07f574fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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dKZTFbiDu1poG5r7wVspTRSoEIlIoPbMgJOsGvvvdWDcwZEhODhkfRE43brD2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\nEEREJPD/AdpmL+fTcCnrAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f07f7ba080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f07f574e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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V586wYkWmo5y9eIMYcc5iFFvzOYvYnJEMJU6MFzgkHDQWfPsPHAgTJ+Y6chFJ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\ntt3J0WpuOaa486tU44bSjV1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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f07fa1f588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy.stats as ss\n",
    "import statsmodels.api as sm\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.optimize import fmin_slsqp\n",
    "%matplotlib inline\n",
    "\n",
    "def riskmetrics_likelihood(parameters, data, sigma2,out = None):\n",
    "        alpha = parameters[0]\n",
    "        beta = parameters[1]\n",
    "        T=len(data)\n",
    "\n",
    "        # Data and Sigma2 are assumed as T by 1 vectors\n",
    "\n",
    "        #sigma2[0]=(np.std(data))**2 #yyf v2\n",
    "        for t in range(1,T):\n",
    "            sigma2[t]=(alpha*data[t-1]**2+beta*sigma2[t-1]) #yyf v2\n",
    "        \n",
    "        logliks = 0.5*(np.log(2*np.pi)+np.log(sigma2)+data**2/sigma2)\n",
    "        loglik = np.sum(logliks)\n",
    "        \n",
    "        if out is None:\n",
    "            return loglik\n",
    "        else:\n",
    "            return loglik, logliks, np.copy(sigma2)\n",
    "        \n",
    "def riskmetrics_constraint(parameters, data, sigma2, out=None):\n",
    "        alpha = parameters[0]\n",
    "        beta = parameters[1]\n",
    "        return np.array([1-alpha-beta])\n",
    "\n",
    "def garch_likelihood_v1(parameters, data, sigma2,out = None):\n",
    "        alpha = parameters[0]\n",
    "        beta = parameters[1]\n",
    "        T=len(data)\n",
    "\n",
    "        # Data and Sigma2 are assumed as T by 1 vectors\n",
    "        \n",
    "        #sigma2[0]=(np.std(data))**2 #yyf v2\n",
    "        omega=sigma2[0]*(1-alpha-beta)\n",
    "        for t in range(1,T):\n",
    "            sigma2[t]=omega+(alpha*data[t-1]**2+beta*sigma2[t-1]) #yyf v2\n",
    "        \n",
    "        logliks = 0.5*(np.log(2*np.pi)+np.log(sigma2)+data**2/sigma2)\n",
    "        loglik = np.sum(logliks)\n",
    "        \n",
    "        if out is None:\n",
    "            return loglik\n",
    "        else:\n",
    "            return loglik, logliks, np.copy(sigma2)\n",
    "\n",
    "def garch_constraint_v1(parameters, data, sigma2, out=None):\n",
    "        alpha = parameters[0]\n",
    "        beta = parameters[1]\n",
    "        return np.array([1-alpha-beta])\n",
    "\n",
    "\n",
    "def mynormqqplot(data):\n",
    "    std_data=data.std()\n",
    "    s_rts = np.sort(data,axis=0)\n",
    "    len_s_rts=len(s_rts)\n",
    "    norm_quant_rts=np.zeros([len_s_rts,1])\n",
    "    for i in range(0,len_s_rts):\n",
    "        norm_quant_rts[i]=ss.norm.ppf((i+1.0-0.5)/len_s_rts)\n",
    "    \n",
    "    plt.figure()\n",
    "    plt.scatter(norm_quant_rts,s_rts)\n",
    "    \n",
    "    min_qt=np.min(norm_quant_rts)\n",
    "    min_s=np.min(s_rts)\n",
    "    min_ax =np.max([min_qt,min_s])\n",
    "    \n",
    "    max_qt=np.max(norm_quant_rts)\n",
    "    max_s=np.max(s_rts)\n",
    "    max_ax =np.min([max_qt,max_s])\n",
    "    \n",
    "    ax_x=np.linspace(min_ax,max_ax,len_s_rts)\n",
    "    ax_y=std_data*ax_x #std_data is like a slope\n",
    "    plt.plot(ax_x,ax_y,'-',color='r')\n",
    "    plt.grid(True)\n",
    "    plt.xlabel('theoretical quantiles')\n",
    "    plt.ylabel('sample quantiles')\n",
    "# mynormqqplot function END\n",
    "\n",
    "df = pd.read_excel(\"yyf_prices.xls\",parse_dates=[0])\n",
    "df.index=df.pop('Date')\n",
    "#df.hist(figsize=[10,10])\n",
    "\n",
    "df_pct_rets = 100* df.pct_change().dropna()\n",
    "df_log_rets =  100* np.log(df.dropna()/df.dropna().shift(1)).dropna()\n",
    "\n",
    "#tmpdata= df_pct_rets.EURUSD\n",
    "#tmpdata= df_pct_rets.SP500\n",
    "#tmpdata= df_pct_rets.SHINDEX\n",
    "#tmpdata= df_pct_rets.SZINDEX\n",
    "\n",
    "\n",
    "#tmpdata= df_log_rets.SZINDEX\n",
    "\n",
    "# other possible data test\n",
    "#tmpdata= df_log_rets.EURUSD\n",
    "#tmpdata= df_log_rets.SP500\n",
    "#tmpdata= df_log_rets.SHINDEX\n",
    "tmpdata= df_log_rets.SZINDEX\n",
    "\n",
    "mean_rts = tmpdata.mean()\n",
    "var_rts  = tmpdata.var()\n",
    "std_rts = tmpdata.std()\n",
    "\n",
    "T=tmpdata.count()\n",
    "\n",
    "sigma2 = np.ones(T)*(var_rts) #initialized volatilities\n",
    "args = (np.asarray(tmpdata),sigma2)\n",
    "analized=1 # or we should set to 252\n",
    "\n",
    "garch_list =['riskmetrics','garch_v1','garch_v2','Ngarch','gjr']\n",
    "garch_type =garch_list[0]\n",
    "\n",
    "if garch_type == 'riskmetrics':\n",
    "# riskmetrics\n",
    "    rm_initial_vals = np.array([0.4,0.96]) ##change\n",
    "    iniloglik,_,_ = riskmetrics_likelihood(rm_initial_vals,np.array(tmpdata), sigma2, out=True)\n",
    "    rm_bounds  =[(0.0,1.0),(0.0,1.0)]\n",
    "    estimates = fmin_slsqp(riskmetrics_likelihood, rm_initial_vals, f_ieqcons=riskmetrics_constraint, bounds=rm_bounds, args =args)\n",
    "    loglik, logliks, sigma2final = riskmetrics_likelihood(estimates,np.array(tmpdata), sigma2, out=True)\n",
    "    print('Initial Values=',rm_initial_vals,'  Initila Likilihood=',iniloglik)\n",
    "elif garch_type == 'garch_v1':\n",
    "# garch v1    \n",
    "    garchv1_initial_vals = np.array([.09,.90]) ##change\n",
    "    iniloglik,_,_ = garch_likelihood_v1(garchv1_initial_vals,np.array(tmpdata), sigma2, out=True)\n",
    "    garchv1_bounds  =[(0.0,1.0),(0.0,1.0)]\n",
    "    estimates = fmin_slsqp(garch_likelihood_v1, garchv1_initial_vals, f_ieqcons=riskmetrics_constraint, bounds=garchv1_bounds, args =args)\n",
    "    loglik, logliks, sigma2final = garch_likelihood_v1(estimates,np.array(tmpdata), sigma2, out=True)\n",
    "    print('Initial Values=',garchv1_initial_vals,'  Initila Likilihood=',iniloglik)\n",
    "elif garch_type == 'garch_v2':\n",
    "# garch v2\n",
    "    garchv2_initial_vals = np.array([mean_rts,.09,.90]) ##change\n",
    "    iniloglik,_,_ = garch_likelihood_v2(garchv2_initial_vals,np.array(tmpdata), sigma2, out=True)\n",
    "    \n",
    "    garchv2_bounds  =[(-10*mean_rts,10*mean_rts),(0.0,0.5),(0.0,1.0)]\n",
    "    estimates = fmin_slsqp(garch_likelihood_v2, garchv2_initial_vals, f_ieqcons=riskmetrics_constraint, bounds=garchv2_bounds, args =args)\n",
    "    loglik, logliks, sigma2final = garch_likelihood_v2(estimates,np.array(tmpdata), sigma2, out=True)\n",
    "    print('Initial Values=',garchv2_initial_vals,'  Initila Likilihood=',iniloglik)\n",
    "elif garch_type == 'Ngarch':\n",
    "# Ngarch    \n",
    "    Ngarch_initial_vals = np.array([0.000005,0.07,0.85,0.50])\n",
    "    iniloglik,_,_ = Ngarch_likelihood(Ngarch_initial_vals,np.array(tmpdata), sigma2, out=True)\n",
    "    \n",
    "    Ngarch_bounds  =[(0.0,0.99),(0.0,0.99),(0.0,1.0),(0.0,1.0)]\n",
    "    estimates = fmin_slsqp(Ngarch_likelihood, Ngarch_initial_vals, f_ieqcons=Ngarch_constraint, bounds=Ngarch_bounds, args =args)\n",
    "    loglik, logliks, sigma2final = Ngarch_likelihood(estimates,np.array(tmpdata), sigma2, out=True)\n",
    "    print('Initial Values=',Ngarch_initial_vals,'  Initila Likilihood=',iniloglik)\n",
    "elif garch_type == 'gjr':\n",
    "#gjr garch\n",
    "    gjr_initial_vals = np.array([mean_rts,var_rts*.01,.03,.09,.90])\n",
    "    iniloglik,_,_ = gjr_garch_likelihood(gjr_initial_vals,np.array(tmpdata), sigma2, out=True)\n",
    "    \n",
    "    finfo=np.finfo(np.float64)\n",
    "    gjr_bounds  =[(-10*mean_rts,10*mean_rts),(finfo.eps,2*var_rts),(0.0,1.0),(0.0,1.0),(0.0,1.0)]\n",
    "    estimates = fmin_slsqp(gjr_garch_likelihood, gjr_initial_vals, f_ieqcons=gjr_constraint, bounds=gjr_bounds, args =args)\n",
    "    print('Initial Values=',gjr_initial_vals,'  Initila Likilihood=',iniloglik)\n",
    "    loglik, logliks, sigma2final = gjr_garch_likelihood(estimates,np.array(tmpdata), sigma2, out=True)\n",
    "else:\n",
    "    print('Sorry we dont have the type you required.')\n",
    "\n",
    "print('Estimated Values=',estimates,'Estimated Likilihood=',loglik)\n",
    "\n",
    "\n",
    "garch_vol=np.sqrt(analized*sigma2final)\n",
    "\n",
    "gr_vol = pd.DataFrame(garch_vol,index=tmpdata.index,columns=['Garch Volatilities'])\n",
    "\n",
    "title_name=garch_type+' volatilities'\n",
    "gr_vol.plot(figsize=(12,7),grid=True,title=title_name)\n",
    "\n",
    "\n",
    "normalized_new_rts=np.asarray(tmpdata)/garch_vol\n",
    "\n",
    "gr_vol.loc[:,'Standerized Returns'] = normalized_new_rts\n",
    "gr_vol.loc[:,'Log Returns'] = tmpdata\n",
    "# call our function ‘mynormqqplot’\n",
    "#gr_vol\n",
    "\n",
    "ttmp=normalized_new_rts/(normalized_new_rts.std())\n",
    "# plot standerized returns using Garch 11\n",
    "#mynormqqplot(ttmp)\n",
    "mynormqqplot(normalized_new_rts)\n",
    "\n",
    "plt.figure()\n",
    "sm.qqplot(normalized_new_rts,line='s')\n",
    "plt.grid(True)\n",
    "plt.xlabel('theoretical quantiles')\n",
    "plt.ylabel('sample quantiles')\n",
    "plt.title('Statsmodels qqplot result')\n",
    "\n",
    "\n",
    "#calculate FOUR MOMENTS of standerized returns using Garch 11\n",
    "a1=ttmp.mean()\n",
    "a2=ttmp.std()\n",
    "a3=ss.skew(ttmp)\n",
    "a4 =ss.kurtosis(ttmp)\n",
    "\n",
    "std_rts=tmpdata.std()\n",
    "normalized_log_rts=np.asarray(tmpdata)/std_rts\n",
    "\n",
    "#calculate FOUR MOMENTS of original log returns\n",
    "b1=normalized_log_rts.mean()\n",
    "b2=normalized_log_rts.std()\n",
    "b3=ss.skew(normalized_log_rts)\n",
    "b4 =ss.kurtosis(normalized_log_rts)\n",
    "\n",
    "print('Type          ||','     mean      ||','     std     ||','      skew     ||','     kurt      ||')\n",
    "print('Original data:  ',b1,b2,b3,b4)\n",
    "print('After Garch:   ',a1,a2,a3,a4)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'riskmetricsriskmetrics'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "garch_type+garch_type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7871.3480058581936"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rm_initial_vals = np.array([.04,.96]) ##change\n",
    "rmloglik,_,_ = riskmetrics_likelihood(rm_initial_vals,np.array(tmpdata), sigma2, out=True)\n",
    "rmloglik"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00034467041816377546"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "var_rts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-2.9044199169311664"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.percentile(tmpdata, 5, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.9474862570498619"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.percentile(tmpdata, 10, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.8359511873235872"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.percentile(tmpdata, 25, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.053345786867628864"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.percentile(tmpdata, 50, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.93288761111017526"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.percentile(tmpdata, 75, axis=0)"
   ]
  }
 ],
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